CVOCA feature extraction method and system based on multi-modal large model

Abstract

The invention discloses a CVOCA feature extraction method and system based on a multi-modal large model, and belongs to the technical field of multi-modal feature extraction. Data preprocessing: processing the input data to generate standardized input; feature unified representation: utilizing a cross-modal unified encoder to map visual, text and time series data to a unified feature space; feature optimization: dynamically adjusting modal weight based on a dynamic feature optimizer to realize efficient feature fusion; carrying out context sensing modeling, and capturing long-range time sequence dependence; carrying out adversarial optimization, and improving the robustness of the model through adversarial training; the feature storage and updating system adopts an expert network and a gating mechanism to realize dynamic updating of a feature library; according to the method, the defects of an existing network behavior analysis technology in the aspects of multi-modal data fusion, calculation efficiency, anti-robustness and dynamic adaptability are overcome, and the technical performance and the application value are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of multimodal feature extraction, and in particular relates to a CVOCA feature extraction method and system based on a multimodal large model. Background Art

[0002] In recent years, multimodal models based on contrastive learning (such as CLIP) have become the core technology for cross-modal alignment. By mapping images and text into a unified semantic space, they enable cross-modal retrieval and classification. However, with the increasing complexity of large model application scenarios (such as network behavior analysis and industrial quality inspection), existing technologies face the following challenges:

[0003] Insufficient long text processing capabilities: Due to the limitation of absolute position encoding, traditional CLIP limits text input to 77 tokens, and the effective semantic length is less than 20 tokens, resulting in the loss of fine-grained information.

[0004] Dynamic feature fusion defects: Existing methods mostly use static feature splicing (such as simply concatenating image and text features), lack context-based adaptive weight allocation, and have difficulty capturing temporal correlations and core attributes in multimodal data.

[0005] Weak adversarial robustness: The feature extraction process is easily interfered by adversarial samples. Existing defense methods rely on data enhancement and lack an active optimization mechanism for the feature space.

[0006] Computational efficiency bottleneck: When processing high-resolution network data (such as the full traffic matrix), the complexity of the traditional Transformer architecture leads to excessive memory usage, making it difficult to meet real-time requirements.

[0007] Secondly, existing technologies like Long-CLIP improve CLIP's long text processing capabilities by combining positional encoding layered interpolation (retaining the first 20 bits and expanding the interpolation to the last 57 bits) with core attribute alignment fine-tuning (PCA extracts key features). However, without a dynamic weighting mechanism, multimodal feature fusion still relies on static splicing, making it difficult to adapt to temporal changes in network behavior. Without feature space gradient constraints, performance against adversarial examples is significantly degraded. Furthermore, when processing high-resolution data, video memory usage still reaches as high as 16GB, making it unsuitable for edge device deployment.

[0008] Existing technologies, such as jina-clip-v1, jointly optimize text-image and text-text contrast losses, using a multi-stage training strategy (short text pre-training, long text fine-tuning, and optimization for difficult negative samples) to improve performance on both cross-modal and text-only tasks. However, independent optimization of text-image and text-text alignment fails to achieve a unified dynamic feature space, limiting the accuracy of multimodal association detection. The three-stage training process is complex, resulting in millisecond-level inference latency, making it unable to meet the real-time response requirements of network behavior analysis.

[0009] Existing lightweight image captioning based on CLIP combines a CLIP encoder with a multi-head sparse attention mechanism, reducing computational effort through Top-K sparsification to achieve lightweight image captioning. However, the attention mechanism is only used within a single modality and does not enable dynamic interactive optimization of image and text features. Furthermore, the lack of an adversarial training module makes the generated captions susceptible to input noise.

[0010] Existing self-trained Chinese CLIP multimodal retrieval involves building a Chinese CLIP model, using an approximate nearest neighbor (ANN) algorithm to achieve rapid cross-modal retrieval and support bidirectional text-image matching. However, the lack of a dynamic weight visualization tool makes it difficult to identify key behavioral features. Furthermore, positional encoding is not optimized, resulting in a 15% drop in long text retrieval accuracy compared to English models.

[0011] Existing network behavior analysis technologies face the following core bottlenecks, which restrict their security protection effectiveness in the era of large models:

[0012] Multimodal data fragmentation: Traditional methods (such as CNN processing traffic images and RNN analyzing log text) use independent models to process data of different modalities. This makes it difficult to achieve cross-modal semantic alignment of multi-source heterogeneous information such as network traffic, operation logs, and system calls, and is unable to capture cross-modal correlation behavior characteristics.

[0013] The contradiction between feature extraction efficiency and resource consumption: When existing large models directly process high-resolution network data (such as full-flow packet capture data), the computational complexity increases quadratically (such as the Transformer's attention mechanism), resulting in excessive memory usage and poor real-time performance.

[0014] Data support: Tests show that the traditional ViT model consumes more than 24GB of GPU memory when processing a 4096×4096 traffic matrix, making it impossible to deploy on edge devices.

[0015] Feature vulnerability in adversarial environments: By injecting noisy traffic (such as DNS tunnel obfuscation) and changing log formats, the robustness of traditional feature extraction models is significantly reduced.

[0016] Technical flaws: Existing methods rely on passive defenses such as data enhancement and lack active adversarial optimization mechanisms in feature space.

[0017] Dynamic behavior patterns lack adaptability: They exhibit long-cycle, low-frequency, and multi-stage characteristics. Traditional feature extraction methods based on fixed time windows have difficulty capturing cross-session context associations, and the missed detection rate is as high as over 35%. Summary of the Invention

[0018] In response to the problems mentioned in the background technology, the present invention proposes a CVOCA feature extraction method and system based on a multimodal large model, focusing on the problems of cross-modal fragmentation, low computational efficiency, vulnerability to adversarial attacks, and insufficient dynamic adaptability in network behavior analysis in the era of large models. Through the CVOCA feature architecture, it achieves technical breakthroughs in multimodal semantic alignment, dynamic resource optimization, active defense enhancement, and context-aware modeling, providing an efficient and reliable network behavior feature extraction solution for the next generation of intelligent security systems.

[0019] Technical solution: In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0020] A CVOCA feature extraction method based on a multimodal large model includes the following steps:

[0021] S1: Data input, including network traffic data, log text data, and system call sequences;

[0022] S2: Data preprocessing: processing network traffic data, log text data, and system call sequences to generate standardized input;

[0023] S3: Unified feature representation, using a cross-modal unified encoder to map visual, textual, and temporal data into a unified feature space;

[0024] S4: Feature optimization, based on a dynamic feature optimizer, dynamically adjusts modal weights and achieves efficient feature fusion;

[0025] S5: Context-aware modeling, using causal Transformer combined with local attention windows to capture long-range temporal dependencies;

[0026] S6: Adversarial optimization, improving model robustness through adversarial training;

[0027] S7: Feature storage and update system, which uses expert network and gating mechanism to realize dynamic update of feature library.

[0028] Preferably, in S2, the specific preprocessing content includes:

[0029] S21: Network traffic visualization: Slice the original traffic data into time windows and convert them into grayscale spectrograms;

[0030] S22: Log text structuring: using regular expressions to extract key fields;

[0031] S23: Constructing a protocol-behavior keyword dictionary: annotating the regular matching extracted fields with protocol keywords;

[0032] S24: System call sequence encoding: mapping the system call ID to a 32-dimensional word vector;

[0033] Generate time series feature blocks through sliding windows.

[0034] As a preference, the specific process of S3 is:

[0035] S31: Visual coding, using the improved ViT-B / 32 model, adding a local sensitive hashing module to the embedding layer to enhance the local features of the traffic spectrum graph;

[0036] S32: Text encoding, based on the BERT architecture, introduces dynamic position encoding;

[0037] S33: Contrastive learning alignment, setting a two-stream contrastive loss function to align visual and text feature spaces;

[0038] S34: Temporal encoding, processed using a Transformer encoder, converts each system call word vector in the sequence into a context-aware temporal feature vector.

[0039] As a preference, the specific process of S4 is:

[0040] S41: Introducing a differentiable gating mechanism to calculate modality importance weights based on the MLP network;

[0041] S42: dynamic feature fusion;

[0042] S43: Resource-aware scheduling: Dynamically adjust the feature slice size based on the real-time status of the device's video memory.

[0043] As a preference, the specific process of S5 is:

[0044] S51: Causal Transformer Architecture: Setting up multi-head attention with causal masks;

[0045] S52: Session-level state tracking: Use LSTM to maintain the session state vector.

[0046] As a preference, the specific process of S6 is:

[0047] S61: Adversarial example generation;

[0048] S62: Calculate the adversarial loss based on the FGSM algorithm.

[0049] As a preference, the specific process of S7 is:

[0050] MoE incremental learning: Setting up a mixture of experts system and calculating each expert’s focus on a specific disturbance type;

[0051] Feature version management: Feature update records are stored based on blockchain to ensure traceability.

[0052] A CVOCA feature extraction system based on a multimodal large model, implementing any of the above CVOCA feature extraction methods based on a multimodal large model, comprising:

[0053] Multimodal input preprocessing module: used to standardize the input heterogeneous data;

[0054] Cross-modal unified encoder: used to map multi-source information into a shared semantic space and complete the initial modeling of semantic associations between modalities;

[0055] Dynamic feature optimizer: Adaptive weight adjustment and redundancy suppression of preliminary features based on real-time feedback mechanism;

[0056] Context-aware modeling module: used to integrate temporal correlation and scene dependency information to construct high-order feature representations with logical coherence;

[0057] Adversarial robustness enhancement module: Improves feature anti-interference capabilities through adversarial training and noise injection strategies;

[0058] Feature storage and update system: used to achieve incremental accumulation and version management of feature knowledge, forming a continuously evolving multimodal feature knowledge base.

[0059] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0060] (1) This invention systematically solves the core pain points of existing network behavior analysis technologies in terms of multimodal data fusion, computational efficiency, adversarial robustness, and dynamic adaptability through the innovative CVOCA multimodal feature extraction architecture, significantly improving technical performance and application value.

[0061] (2) This invention breaks through the fragmentation of multimodal data and achieves efficient semantic alignment. Traditional methods use independent models to process multimodal data such as vision, text, and time series, resulting in a cross-modal correlation feature loss rate of up to 35%, making it difficult to capture the deep correlation between multimodal data. This invention uses a two-stream contrastive learning framework to map multimodal data to a unified feature space and achieve cross-modal semantic alignment: in the CTU-13 dataset test, the cross-modal detection accuracy increased to 98.7% (compared to 91.2% for traditional methods), an improvement of 7.5%.

[0062] In existing technologies, the correlation pattern between abnormal software encryption behavior (text logs) and abnormal traffic surges (visual spectrum graphs) was successfully identified, and the detection rate was improved by 27.4%.

[0063] (3) This invention significantly improves computing efficiency and adapts to edge deployment. When existing large models process high-resolution network data, the graphics memory usage is too high and the computing latency is long, which cannot meet the resource constraints and real-time requirements of edge devices. This invention introduces a layered feature slice injection and dynamic resource scheduling mechanism to significantly optimize computing efficiency: when processing a 4K×4K traffic matrix, the GPU graphics memory usage is reduced to 9.2GB (compared to 24GB for traditional methods), a 62% reduction. Inference latency is shortened from seconds to milliseconds (less than 15ms), meeting real-time detection requirements. On edge devices (ARM Cortex-A72), the feature extraction speed reaches 153 frames / second (compared to 52 frames / second for traditional methods), an improvement of 194%.

[0064] (4) This invention achieves enhanced adversarial robustness. The detection accuracy of the traditional feature extraction model dropped sharply to 54.3%, and it was unable to effectively deal with advanced methods such as DNS tunnel obfuscation and HTTP requests. This invention significantly improved the robustness of the model through feature space gradient reversal layer (GRL) and adversarial distillation training: in the adversarial test set with injected noise traffic and altered logs, the detection accuracy remained at 89.5% (compared to 54.3% of the traditional method), an improvement of 64.8%. The external defense success rate increased by 65.2%.

[0065] (5) This invention improves dynamic adaptability by using MoE incremental learning and dynamic prompt engineering to enhance the model's dynamic adaptability. After importing new feature templates, the model update time is shortened to 2 hours (compared to 72 hours with traditional methods), a 96% speed increase. The early recognition rate for APTs is increased to 82.4% (compared to less than 50% with traditional methods). The USB offline update mechanism supports rapid loading of the latest feature library on edge devices even in an offline environment.

[0066] (6) This invention can optimize user experience and improve operation and maintenance efficiency. The existing system is complex to operate, and security analysts need to manually analyze multi-source data, resulting in a high false alarm rate and a heavy operation and maintenance burden. This invention optimizes user experience through a visual dashboard and one-click policy deployment. The average response time is shortened to less than 5 minutes (traditional methods are >30 minutes), a reduction of 83.3%. The false alarm rate is reduced to 1.2% (traditional methods are 5.8%), significantly reducing the ineffective workload of operation and maintenance personnel.

[0067] (7) This invention addresses the core issues of existing technologies, such as multimodal data fragmentation, low computational efficiency, vulnerability to adversarial attacks, and insufficient dynamic adaptability. By employing technologies such as cross-modal unified encoding, hierarchical feature optimization, adversarial training, and dynamic learning, it achieves a comprehensive improvement in the effectiveness of network behavior analysis. The above technical effects have been verified in experimental data and actual scenarios, forming a complete closed loop from problem identification to technological breakthroughs and implementation of results, fully demonstrating the practical value and innovation of this invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 Schematic diagram of the CVOCA multimodal feature extraction system architecture of the present invention;

[0069] Figure 2 It is a schematic diagram of the front-end interaction logic of the present invention;

[0070] Figure 3 is a schematic diagram of the background processing logic of the present invention;

[0071] Figure 4 It is a data processing flow diagram of the present invention;

[0072] Figure 5 is a network topology diagram of the CVOCA system of the present invention;

[0073] Figure 6 It is a CVOCA feature extraction timing diagram of the present invention. DETAILED DESCRIPTION

[0074] The present invention will be further illustrated below with reference to specific examples. The examples are implemented based on the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0075] Example 1

[0076] The special abbreviations or custom terms used in this embodiment are explained as follows:

[0077] CVOCA: Cross-modal Volumetric Optimization Context-aware Adversarial Robustness, a multimodal feature extraction architecture that integrates cross-modal unified encoding, dynamic feature optimization, context-aware modeling, and adversarial robustness enhancement techniques to improve the accuracy and robustness of network behavior analysis.

[0078] CLIP: Contrastive Language-Image Pre-training. A neural network model trained on a large-scale image-text pair dataset through contrastive learning, capable of learning semantic associations between images and text.

[0079] ViT: Vision Transformer, an image encoder based on the Transformer architecture, is used to extract image features. In this paper, ViT is used to process network traffic image data.

[0080] BERT (Bidirectional Encoder Representations from Transformers) is a text encoder based on the Transformer architecture for extracting text features. In this paper, BERT is used to process text log data from network activities.

[0081] InfoNCE: Information Noise-Contrastive Estimation, a commonly used contrastive loss function for learning to distinguish positive and negative samples. In this paper, InfoNCE is used for contrastive learning in cross-modal unified encoding.

[0082] GPU: Graphics Processing Unit, a graphics processor, is an electronic circuit specialized for parallel computing, often used to accelerate deep learning tasks, such as model training and inference in this invention.

[0083] Prompt Engineering: A technique that uses specific text prompts to guide a pre-trained language model to perform specific tasks. In this paper, Prompt Engineering is used to dynamically generate protocol-related semantic features.

[0084] NLP (Natural Language Processing) is a branch of computer science and artificial intelligence that studies how to enable computers to understand and process human language. In this paper, NLP technology is used to process text data from online activities.

[0085] CNN: Convolutional Neural Network, a deep learning model commonly used in image processing and computer vision tasks. In this paper, CNN can be used as an alternative to ViT for image feature extraction.

[0086] Transformer: A neural network architecture based on the self-attention mechanism that has achieved great success in natural language processing and computer vision. In this paper, the Transformer architecture is used for text encoding and context-aware modeling.

[0087] CUDA: Compute Unified Device Architecture, a parallel computing platform and programming model launched by NVIDIA, used for general computing on GPUs, supporting the acceleration of deep learning tasks in this invention.

[0088] like Figures 1-4 As shown, the CVOCA feature extraction method based on the multimodal large model provided in this embodiment includes the following steps:

[0089] S1: data input;

[0090] Input network traffic data, log text data, and system call sequences. Network traffic: Mirrored to the network card via the SPAN port and captured using libpcap. Operation logs: syslog-ng collects them to / var / log / . System calls: The strace tool captures process behavior.

[0091] libpcap: Packet Capture Library, is an open source, cross-platform network packet capture library, which is a fundamental component in the field of network analysis and security tools.

[0092] syslog-ng: System Logging - Next Generation, is an open source log management tool for Linux / Unix systems. It aims to replace the traditional syslogd daemon and provide more powerful, flexible, and reliable log collection, transmission, processing, and storage capabilities.

[0093] S2: data preprocessing;

[0094] Utilize the multimodal input preprocessing module to process multi-source data such as network traffic, log text, system calls, and generate standardized input.

[0095] Network traffic visualization: Slice the original traffic data (such as PCAP) by time window (default 1 second) and convert it into a grayscale spectrum graph, specifically:

[0096]

[0097] Among them, M i , jRepresents the calculated value of the cell at coordinate (i, j) in the generated two-dimensional matrix (grayscale spectrum). This value usually corresponds to the intensity of a pixel in the image. i The count() function calculates the aggregate value defined by the expression in the parentheses. In the context of network traffic analysis, this usually refers to counting or summing the attributes of network packets that meet specific conditions. size Indicates the size of a single network data packet, usually in bytes. weight [j] represents the weight value associated with the jth protocol type. According to the document description, this weight table is defined according to the RFC standard.

[0098] libpcap parses traffic - time window slices (1 second) - generates grayscale spectrogram.

[0099] Log text structuring: Use regular expressions to extract key fields (such as IP address, HTTP method, and return code).

[0100] Build a protocol-behavior keyword dictionary (including key operation tags for 14 types of protocols such as HTTP / HTTPS / DNS).

[0101] Regular expression matching to extract fields - annotate protocol keywords (such as HTTP GET).

[0102] System call sequence encoding: Maps system call IDs to 32-dimensional word vectors (pre-trained on the Linux kernel call dataset).

[0103] ID is mapped to 32-dimensional word vector-time series block (window 256).

[0104] The time series feature blocks are generated by sliding windows (window size 256, step size 64).

[0105] S3: unified feature representation;

[0106] A cross-modal unified encoder is used to map visual, textual, and temporal data into a unified feature space.

[0107] Visual encoding: Use the ViT-LSH model (pre-trained on ImageNet+Internal Traffic dataset).

[0108] ImageNet is a large visual database (dataset) designed for research in visual object recognition software. It contains over 14 million manually annotated images covering over 20,000 categories. In the field of deep learning, especially computer vision, ImageNet is widely used as a dataset for model pre-training.

[0109] The improved ViT-B / 32 model is used, and the locality-sensitive hashing (LSH) module is added to the embedding layer to enhance the local features of the traffic spectrum graph:

[0110]

[0111] in, is the weight matrix; LSH(x) represents the result of performing a locality-sensitive hashing transformation on the input vector x. x represents the input vector, which represents the high-dimensional data point to be hashed, such as user behavior characteristics, network traffic characteristics, etc. sign represents the sign function, which maps the output of the hash function to a binary hash code of {-1, 1}. b hash Represents the bias term, which is used to adjust the threshold of the hash function so that the hash function can adapt more flexibly to different data distributions and improve the discrimination of the hash code. hash Represents the weight matrix, which is used to map the input vector x to the hash space and define the direction of the hash function so that similar input vectors have similar hash codes in the hash space.

[0112] Text encoding: Use the BERT-DPE model (pre-trained on Wikipedia + security log corpus).

[0113] Based on the improvement of BERT architecture, dynamic position encoding (DPE) is introduced, specifically:

[0114]

[0115] Among them, a (pos,i) A learnable scaling factor to adapt to different protocol text lengths; PE (pos,2i) Represents the positional encoding value of dimension 2i at position pos. pos is a position index, which indicates the position of an element in a sequence. For example, in natural language processing, it represents the position of a word in a sentence. i indicates the encoding value used to specify which dimension is currently being calculated. 2i represents an even index of the dimension. In positional encoding, sine and cosine functions are usually used to generate encodings of different dimensions. 2i is used to select the dimension corresponding to the sine function. modelRepresents the dimension of the model, that is, the length of the position encoding vector. In models such as Transformer, the length of the position encoding vector is usually the same as the hidden layer dimension of the model.

[0116] Contrastive Learning to Align:

[0117] Set the dual-stream contrast loss function L align , forcing the visual and text feature spaces to be aligned, specifically:

[0118]

[0119] Where S() is the cosine similarity, τ=0.05, N is the batch size; v i Represents the visual feature vector of the i-th sample. In multimodal learning, visual features usually come from visual data such as images and videos. i Represents the text feature vector of the i-th sample. In multimodal learning, text features usually come from text data such as text descriptions and labels. j Represents the text feature vector of the jth sample. When calculating the alignment loss, it is necessary to consider the similarity between the current visual feature and all other text features, so j is used to traverse all text features.

[0120] Temporal Encoding: Uses Temporal Convolutional Network (TCN). A TCN is a neural network architecture specifically designed to process sequential data (such as time series, natural language, audio, video, etc.).

[0121] The specific process of temporal coding:

[0122] Input preparation: Receive the preprocessed system call sequence data, that is, the sequence that has been mapped to a 32-dimensional word vector.

[0123] These vector sequences are usually organized into blocks of temporal features generated by sliding windows mentioned in building the protocol-behavior keyword dictionary, where each block represents the system call activity within a time segment.

[0124] Coding architecture selection and core processing:

[0125] A Transformer-based architecture is used as the core encoder. This echoes the causal Transformer architecture used in context-aware modeling, indicating that temporal encoding is its foundation or component.

[0126] Core operations:

[0127] Self-attention mechanism: The Transformer encoder layer uses a self-attention mechanism to calculate the relationship weights between each system call vector (word vector) and all other call vectors in the sequence. This enables the model to capture long-range dependencies and complex patterns between calls, regardless of their position in the sequence.

[0128] Positional encoding: Since the Transformer itself does not contain sequence order information, positional encoding is required. This can be standard sine / cosine encoding or a variant of dynamic positional encoding mentioned in text encoding to inject the absolute or relative position information of each call in the sequence into the model.

[0129] Feedforward Neural Network: Each encoder layer also contains a feedforward neural network (FFN) that performs nonlinear transformations on the output of self-attention to increase the expressive power of the model.

[0130] Residual Connection and Layer Normalization: Residual Connection and Layer Normalization are applied after each sub-layer (self-attention and FFN) to stabilize the training process and promote the learning of deep networks.

[0131] Output representation: After being processed by several layers of Transformer encoders, each system call word vector in the sequence is converted into a context-aware temporal feature vector.

[0132] These output vectors not only contain the original call semantic information (from word embeddings), but also incorporate the temporal dependency information of the call in the context of the entire sequence block.

[0133] The output of the entire sequence block can be represented as a series of context vectors, or a fixed-length temporal feature vector can be obtained through pooling (such as average pooling, maximum pooling, or taking the vector at the beginning / special marker of the sequence) for subsequent multimodal fusion.

[0134] Integration with the overall plan:

[0135] Unified feature space: The output of the temporal encoder (context vector or pooled feature vector) needs to be aligned with other modalities (such as visual features, text features) in terms of dimension and semantics so that it can enter a "unified feature space." This usually means that the dimension of the output vector needs to match the output dimension of the encoder of the other modality (for example, both are mapped to 256 or 512 dimensions).

[0136] Laying the foundation for high-level modeling: The context-aware features obtained through temporal encoding are the direct input to "context-aware modeling" (a causal Transformer combined with LSTM for session-level state tracking). The temporal encoder is responsible for extracting basic, fine-grained inter-call dependencies, while the context-aware modeling module may be responsible for integrating this information over longer time scales or across sessions.

[0137] S4: Feature optimization;

[0138] Traditional static feature fusion cannot adapt to the dynamic changes in network behavior. This invention is based on a dynamic feature optimizer, dynamically adjusts the modal weights, achieves efficient feature fusion, and uses a differentiable gating mechanism to calculate the modal importance weights, specifically:

[0139]

[0140] Among them, W m Indicates the attention weight of the mth element (such as a word, image region, etc.). In the attention mechanism, the weight is used to indicate the importance or relevance of the element. m is the modal feature, C is the global context vector; The concatenation operation is performed. MLP represents a multi-layer perceptron, which is used to map the feature vector and context vector to the space of attention weights. Softmax represents the softmax function, which is used to convert the output of the multi-layer perceptron into a probability distribution, ensuring that the sum of the attention weights of all elements is 1.

[0141] Feature fusion, specifically:

[0142]

[0143] Among them, Proj m is the modal projection layer, f fusion In multimodal fusion, it is usually necessary to fuse the feature vectors of different modalities into a unified representation for subsequent task processing. fusion represents the result of the fusion. m represents the index or number of the element, which is used to traverse and identify the elements that need to be fused. m Indicates the attention weight of the mth element. In the attention mechanism, the weight is used to indicate the importance or relevance of the element and is used to perform weighted fusion of feature vectors so that important elements occupy a larger proportion in the fused feature vector. m is the modal feature.

[0144] Resource-aware scheduling: Dynamically adjust the feature slice size based on the real-time status of the device's video memory.

[0145] In resource-aware scheduling:

[0146] 1) Input parameters include matrix and mem_status. Matrix represents the high-resolution network traffic spectrum to be processed, and mem_status indicates the real-time status of the device's video memory. A value of 'high' indicates sufficient video memory (e.g., ≥16GB on the server side). Other values ​​(e.g., 'low') indicate limited video memory (e.g., ≤8GB on the edge device).

[0147] 2) The judgment logic is: max_patch = 128 if mem_status == 'high' else 64.

[0148] When the video memory is sufficient (mem_status=='high'): Set the slice size to 128×128. Large slices reduce the number of calculations and improve processing efficiency. When the video memory is insufficient (other states): Set the slice size to 64×64. Small slices reduce the single video memory usage and avoid overflow.

[0149] 3) Perform the actual slicing by calling the split_matrix() function, splitting the original matrix into several sub-blocks according to the calculated size and outputting them.

[0150] S5: context modeling;

[0151] Utilize the context-aware modeling module to capture long-term dependencies across sessions.

[0152] Input: fusion feature sequence .

[0153] Causal Transformer Architecture:

[0154] Constructing multi-head attention with causal mask:

[0155]

[0156] Among them, M causal is the causal mask matrix, which ensures temporal dependency. Q represents the query matrix, which is used to represent the features of the element or position that currently needs attention. K represents the key matrix, which is used to represent the features of all elements or positions in the sequence. By calculating similarity with Q, attention weights are generated. V represents the value matrix, which contains the actual information of all elements or positions in the sequence. The weighted summation is performed according to the attention weights to generate the final output. T represents the transpose operation; d k Indicates the dimension of the key (Key dimension). When calculating the attention weight, the dot product result will be scaled to prevent the softmax function from saturating. Mcausal Represents the causal mask matrix, which ensures that only the current position and the previous position are considered when calculating attention, preventing the model from seeing future information. Represents element-wise multiplication, also known as the Hadamard product, which multiplies the elements of corresponding positions in two matrices to obtain a new matrix.

[0157] Introducing the local attention window (window size 256), the computational complexity is reduced from down to .

[0158] Session-level state tracking: Use LSTM to maintain the session state vector. The specific calculation formula is:

[0159]

[0160] Among them, f t is the current moment feature, h t is the hidden state; h t-1 Represents the hidden state at the previous moment. In LSTM networks, the hidden state is used to convey long-term dependencies in the sequence and contains all information up to the previous moment. LSTM networks use mechanisms such as update gates, forget gates, and output gates to control the flow and memory of information, thereby capturing long-term dependencies in the sequence.

[0161] Output: Context-enhanced features .

[0162] S6: Adversarial optimization;

[0163] Based on the adversarial robustness enhancement module, the model robustness is improved through adversarial training.

[0164] Adversarial sample generation, specifically:

[0165]

[0166] in, represents the input adversarial perturbation, which is added to the original input sample to generate adversarial samples, thereby misleading the model to make wrong predictions. It is a hyperparameter that controls the size of the perturbation. The adversarial examples generated by the value are more difficult to be detected by the model, but may also have less impact on the model's prediction. sign represents the symbol function, which is used to extract the gradient The sign information of , that is, only the direction of the gradient is retained, and its magnitude is ignored. represents the gradient operator, which acts on the loss function L and calculates the gradient with respect to the input x. L represents the loss function, which quantifies the difference between the model's prediction and the true label. f(x) represents the model's prediction for input x. y represents the true label or expected output for input x.

[0167] Based on the FGSM algorithm, calculate the adversarial loss , specifically:

[0168]

[0169] in, Represents the adversarial perturbation of the input. It is typically added to the input data to generate adversarial examples, thereby misleading the model into making incorrect predictions. f(x) represents the output value after processing or mapping the input x. In adversarial training, this typically represents the model's prediction for the original input x. f(x+δ) represents the new output after adding a perturbation δ to the input. In adversarial training, this represents the model's prediction for the input x+δ after the adversarial perturbation has been added.

[0170] S7: Feature storage and update system;

[0171] Feature update and version management, supporting incremental update and version management of feature libraries.

[0172] Storage format: HDF5 (feature vector) + JSON (metadata).

[0173] Version control: Git-like branch management, support rollback.

[0174] MoE incremental learning: Set up a mixture of 8 experts, each expert focuses on a specific disturbance type:

[0175]

[0176] Among them, g i For the gated network, E i is the expert network; update Represents the updated value. This value is usually calculated using some update rule or algorithm and is used to reflect the latest state of the system or model at a certain moment.

[0177] like Figure 1As shown, the CVOCA feature extraction system based on a multimodal large model provided in this embodiment builds an end-to-end intelligent processing architecture, the core of which is driven by six major modules: first, the multimodal input preprocessing module realizes the standardized alignment and noise filtering of heterogeneous data such as text, images, and audio; then, a cross-modal unified encoder is used to map multi-source information into a shared semantic space to complete the initial modeling of semantic associations between modalities; on this basis, the dynamic feature optimizer performs adaptive weight adjustment and redundancy suppression on the preliminary features based on a real-time feedback mechanism; then, the context-aware modeling module incorporates temporal association and scene dependency information to construct a high-order feature representation with logical coherence; to ensure system security, the adversarial robustness enhancement module improves the feature anti-interference ability through adversarial training and noise injection strategies; finally, the feature storage and update system realizes the incremental accumulation and versioning management of feature knowledge, forming a continuously evolving multimodal feature knowledge base. Through the closed-loop setting of modal fusion, dynamic optimization and knowledge precipitation, the system significantly improves the feature expression quality and decision reliability of multimodal data in complex scenarios.

[0178] Multimodal input preprocessing module:

[0179] Technical issues: Network behavior data is highly noisy and multi-format (PCAP traffic packets, JSON logs, system call sequences, etc.), and requires standardized processing.

[0180] The specific implementation content includes:

[0181] Network traffic visualization: Slice raw traffic data (such as PCAP) by time window (default 1 second) and convert it into a grayscale spectrum graph.

[0182] Log text structuring: Use regular expressions to extract key fields.

[0183] Build a protocol-behavior keyword dictionary (including key operation tags for 14 types of protocols such as HTTP / HTTPS / DNS).

[0184] System call sequence encoding: Maps system call IDs to 32-dimensional word vectors (pre-trained on the Linux kernel call dataset).

[0185] The time series feature blocks are generated by sliding windows (window size 256, step size 64).

[0186] Cross-modal unified encoder: solves the semantic gap problem among visual, textual, and temporal modalities, and maps visual, textual, and temporal data into a unified feature space.

[0187] The specific implementation content includes: visual encoding branch, text encoding branch, contrastive learning alignment; using a two-stream contrastive learning framework to map multimodal data into a unified feature space.

[0188] Based on the improved ViT (visual encoder) and BERT (text encoder), semantic alignment is achieved by combining locality sensitive hashing (LSH) and dynamic position encoding (DPE).

[0189] Dynamic Feature Optimizer:

[0190] Technical problem: Traditional static feature fusion cannot adapt to the dynamic changes of network behavior.

[0191] The specific implementation content includes: introducing a differentiable gating mechanism to dynamically adjust the fusion weights of each modal feature; calculating the modal importance weights based on the MLP network to achieve adaptive feature fusion; resource-aware scheduling: dynamically adjusting the feature slice size according to the real-time status of the device's video memory.

[0192] Dynamically adjust the modality importance to improve fusion efficiency.

[0193] Context-aware modeling module:

[0194] Technical issues: Traditional methods have difficulty capturing long-term dependencies across sessions.

[0195] Implementation: A causal Transformer is combined with a local attention window to capture long-range temporal dependencies. Causal masks and session-level state tracking (LSTM) are used to optimize temporal logic and computational efficiency.

[0196] Causal Transformer Architecture:

[0197] Set up multi-head attention with causal masking:

[0198]

[0199] in, is a lower triangular mask matrix to ensure temporal dependency. Q represents the query matrix, which is used to represent the features of the element or position that needs to be focused on. K represents the key matrix, which is used to represent the features of all elements or positions in the sequence. The attention weights are generated by calculating the similarity with Q. V represents the value matrix, which contains the actual information of all elements or positions in the sequence. The weighted summation is performed according to the attention weights to generate the final output. T represents the transpose operation; d k Indicates the dimension of the key (Key dimension). When calculating the attention weight, the dot product result will be scaled to prevent the softmax function from saturating. M causal Represents the causal mask matrix, which ensures that only the current position and the previous position are considered when calculating attention, preventing the model from seeing future information. Represents element-wise multiplication, also known as the Hadamard product, which multiplies the elements of corresponding positions in two matrices to obtain a new matrix.

[0200] Introducing the local attention window (window size 256), the computational complexity is reduced from down to .

[0201] Session-level state tracking: Use LSTM to maintain the session state vector. The specific calculation formula is:

[0202]

[0203] Among them, f t is the current moment feature, h t is the hidden state; h t-1 Represents the hidden state at the previous moment. In LSTM networks, the hidden state is used to convey long-term dependencies in the sequence and contains all information up to the previous moment. LSTM networks use mechanisms such as update gates, forget gates, and output gates to control the flow and memory of information, thereby capturing long-term dependencies in the sequence.

[0204] Adversarial robustness enhancement module: embeds the gradient reversal layer (GRL) and constructs an adversarial distillation training framework.

[0205] This application improves the defense capability of the model through adversarial training objective function.

[0206] Implementation: Define a neural network module named DynamicFusion, which inherits from nn.Module and aims to achieve dynamic fusion of three modal features: visual, textual, and sequence. The gradient reversal coefficient λ is 0.1. The specific process includes function input, return, and specific judgment:

[0207] 1. Class definition and initialization:

[0208] Use class DynamicFusion(nn.Module) to define a class named DynamicFusion, which inherits from PyTorch's nn.Module class, indicating that this is a neural network module.

[0209] Use def __init__(self, dim), the class initialization function, which receives a parameter dim, which represents the dimension of the input feature.

[0210] Use super().__init__() to call the initialization function of the parent class.

[0211] Use self.mlp = nn.Sequential(...) to define a multi-layer perceptron (MLP) with two fully connected layers and a ReLU activation function. The first fully connected layer converts the input dimension from (Because the input is the concatenation of three modal features) mapped to dim, the second fully connected layer maps the dimension from dim to 3 and outputs three weight values, corresponding to the fusion weights of the three modalities of vision, text and sequence.

[0212] 2. Forward propagation function:

[0213] Use def forward(self, visual, text, seq) to define the forward propagation function, which receives three parameters, including visual, text, and seq, which represent the features of the three modalities of vision, text, and sequence, respectively.

[0214] Use context = torch.cat([visual, text, seq], dim=-1) to concatenate the three modal features of the input on the last dimension to form a joint feature representation context.

[0215] Use weights = F.softmax(self.mlp(context), dim=-1) to input the spliced ​​feature context into the MLP to obtain the fusion weights of the three modalities, and then normalize them through the softmax function to ensure that the sum of the three weights is 1.

[0216] Use fused = weights[:,0].unsqueeze(1) visual + weights[:,1].unsqueeze(1) text + weights[:,2].unsqueeze(1) seq, uses the normalized weights to perform weighted summation on the features of the three modalities to obtain the fused feature. Here, weights[:,0], weights[:,1], and weights[:,2] represent the fusion weights of the visual, textual, and sequence modalities, respectively. unsqueeze(1) is used to adjust the dimension of the weights so that they can be broadcast multiplied with the features.

[0217] Finally, the fused feature is returned.

[0218] 3. Specific judgment process:

[0219] During the forward propagation process, the features of the three modalities are first concatenated to form a more comprehensive contextual representation.

[0220] Then, the mapping from this context representation to the fusion weights of the three modalities is learned through MLP. The structural setting of MLP enables it to capture the complex interaction between different modalities and output fusion weights suitable for the current input.

[0221] Finally, the features of the three modalities are fused into a unified feature representation through weighted summation. This fusion method not only retains the unique information of each modality, but also enables information interaction and complementarity between the modalities through weight adjustment.

[0222] Implementation: Define a function called adversarial_loss to calculate the difference between the model's predictions for the input data and its adversarial examples, i.e., the adversarial loss. This includes the function input, return, and specific judgment process:

[0223] 1. Function definition and input parameters:

[0224] The function is defined as def adversarial_loss(model, x, y, epsilon=0.01) and takes four parameters: model: the neural network model to be evaluated. x: the input data, typically the original input to the model. y: the true label, typically used to calculate a standard loss (such as cross-entropy loss). epsilon: the magnitude of the adversarial perturbation, which controls how different the adversarial example is from the original input. The default value is 0.01.

[0225] 2. Generate adversarial samples (problems):

[0226] By x_adv = x + epsilon torch.sign(x.grad) attempts to generate an adversarial sample x_adv. However, there is a key problem here: x.grad is undefined before forward propagation and backpropagation. The correct adversarial sample generation process should include:

[0227] a. Perform a forward pass on the input x to obtain the model prediction.

[0228] b. Calculate the loss function based on the predicted results and the true label (or target label).

[0229] c. Backpropagate the loss function and calculate the gradient of the input x.

[0230] d. Generate adversarial samples x_adv based on gradient information.

[0231] 3. Model prediction (disable gradient calculation):

[0232] With torch.no_grad(), the context manager is used to disable gradient calculation to reduce memory consumption and speed up the calculation, because only forward propagation is required at this stage and no backpropagation is required.

[0233] Use f_x = model(x) to perform forward propagation on the original input x to obtain the model's prediction result f_x.

[0234] Use f_adv = model(x_adv) to perform forward propagation on the generated adversarial sample x_adv to obtain the prediction result f_adv of the model adversarial sample.

[0235] 4. Calculate adversarial loss:

[0236] Through loss = F.mse_loss(f_x, f_adv), the mean squared error (MSE Loss) between the original input prediction result f_x and the adversarial example prediction result f_adv is calculated. This loss function measures the degree of difference between the model's prediction results for the original input and the adversarial example.

[0237] 5. Return loss value:

[0238] The function returns the calculated adversarial loss value loss, which can be used to evaluate the robustness of the model to adversarial perturbations, or to optimize the model as part of the training process to improve its adversarial robustness.

[0239] Active defense in feature space improves robustness to 89.5%.

[0240] The adversarial training objective function is:

[0241]

[0242] The first term constrains feature smoothness, the second term enforces perturbation consistency, and λ represents a hyperparameter used to balance the weights of the gradient norm term and the KL divergence term in the loss function. By adjusting the value of λ, this application can control the trade-off between model complexity and robustness. λ = 0.5; E(x,y) represents the expected value, which is the average of all possible input-label pairs (x,y). In adversarial training, this application typically considers all possible input and label combinations to evaluate the model's performance across the entire data distribution. F represents the Frobenius norm. represents adversarial perturbations, which use FGSM to generate subtle input noise, simulating DNS tunnel obfuscation and log alteration. f(x) represents the feature extraction function, the core model of CVOCA, which maps multimodal inputs (traffic, logs, and call sequences) into a unified feature vector. x represents the raw input, preprocessed and standardized data (e.g., 4096×4096 grayscale traffic images or structured log text). K represents the feature dimension, the dimension of the feature vector f(x) (e.g., 1024 for visual input and 768 for text), which determines the computational space for the KL divergence. L represents the adversarial loss, an objective function that quantifies the robustness of the model; smaller values ​​indicate greater adversarial resistance. Represents the gradient operator, which calculates the partial derivative (Jacobian matrix) of the feature f(x) with respect to the input x, and measures the sensitivity of the feature.

[0243] Feature storage and update system: A hybrid expert system (MoE) is used to support online learning of the feature extraction module.

[0244] Technical key: Expert network and gating mechanism realize dynamic update of feature library.

[0245] Specific contents include: MoE incremental learning and feature version management.

[0246] Feature version management: Feature update records are stored on the blockchain to ensure traceability. A class called FeatureVersionControl is defined to implement feature version management. The core idea is to store feature update records on the blockchain to ensure traceability. This includes the specific function input, return, and specific judgment process:

[0247] 1. Class definition and initialization:

[0248] Define a class named FeatureVersionControl, that is, class FeatureVersionControl, which encapsulates the function of feature version control.

[0249] Define the class initialization function (constructor), that is, def __init__(self, blockchain), which receives a parameter blockchain, which represents a pre-created blockchain object.

[0250] During initialization, the passed blockchain object is assigned to the instance variable self.chain. This enables instances of the FeatureVersionControl class to access and operate this blockchain to store feature update records.

[0251] 2. Submit the feature hash value to the blockchain (commit function):

[0252] Use def commit(self, feature_hash) to define a method called commit that accepts a parameter called feature_hash, which represents the hash value of the feature to be recorded. Hash values ​​are typically used to uniquely identify the content of a feature; even slight changes to the feature content will result in a significantly different hash value.

[0253] The core operation of the commit method is block = create_block(feature_hash, self.chain[-1].hash). It calls a hypothetical, already defined function, create_block. This function creates a new blockchain block. feature_hash is stored as the data content of the new block, recording the features currently requiring version control. self.chain[-1].hash represents the hash value of the last (most recent) block in the blockchain. Including the hash value of the previous block in the new block is a key mechanism in blockchain technology to ensure data immutability and a chain-like structure. Any attempt to modify historical data in the blockchain will result in a change in the hash values ​​of all subsequent blocks, making it easily detectable.

[0254] Use self.chain.append(block) to append the newly created block to the end of the self.chain blockchain. This step permanently records the feature update to the blockchain. Due to the immutable nature of the blockchain, once the feature hash is written to the blockchain, it is difficult to modify or delete, ensuring the traceability and trustworthiness of the feature version history.

[0255] Hierarchical feature injection and resource optimization: Set up hierarchical slicing injection technology to dynamically allocate high-resolution data to different layers of the model.

[0256] Technical key: Adaptive slicing and local block injection to optimize edge device deployment efficiency.

[0257] Hierarchical feature injection (edge ​​devices): Define a function called hierarchical_injection to implement hierarchical feature injection technology to optimize the deployment efficiency of the model on edge devices. This includes function input, return, and specific judgment process:

[0258] 1. Function definition and input parameters:

[0259] The function is defined as def hierarchical_injection(x, model, mem_status) and takes three parameters: x: the input data, typically an image or feature map, which is the raw data to be processed. model: the neural network model where the function will perform feature injection at different layers. mem_status: a memory status indicator used to determine the size of the extracted patch to accommodate different resource constraints.

[0260] Patch extraction: Dynamically determines the patch size based on the memory status. If the memory status is 'low' (indicating limited memory resources), a smaller patch size (32x32) is used to reduce memory usage; otherwise, a larger patch size (64x64) is used to capture richer feature information. Patches of the specified size are then extracted from the input data x. These patches are injected into different layers of the model as additional feature information. The extract_patches function implements the specific logic for patch extraction.

[0261] Layered feature injection: This method traverses each layer of the model and prepares for feature injection. The key logic of layered injection is implemented through the if i % 2 == 0 statement, which injects features every other layer. This setting aims to balance the frequency of feature injection with the processing efficiency of the model, avoiding excessive feature injection that may lead to performance degradation.

[0262] Use inject_patch = patches[i / / 2] to select the patch to be injected based on the current layer. Since injection is performed on every other layer, the patch array patches can be indexed by i / / 2 to ensure that each injected layer receives a unique patch.

[0263] Use layer_input = torch.cat([layer_input, inject_patch], dim=1) to inject the patch into the input of the current layer. This assumes that layer_input is defined outside the loop and is constructed incrementally within each iteration, representing the input data before processing the current layer. Using the torch.cat function, the patch inject_patch is concatenated with the original input layer_input along the specified dimension (dim=1) to achieve feature fusion.

[0264] Use layer_output = layer(layer_input) to pass the injected input to the current layer for processing and obtain the output layer_output of the current layer.

[0265] Return output: The function returns the output layer_output of the last layer, which is the final output result of the model after layered feature injection processing.

[0266] Through the aforementioned hardware and software configuration, solution logic setup, and data flow optimization, this invention significantly outperforms existing technologies in cross-modal feature extraction efficiency, robustness to adversarial forces, and edge computing adaptability, providing efficient and reliable technical support for network behavior analysis. It also reduces graphics memory usage by 62%, making it suitable for resource-constrained scenarios.

[0267] The system in this embodiment can be applied in scenarios of financial transactions and industrial Internet of Things intrusion detection.

[0268] Financial Transactions:

[0269] Input: Visual mode: screenshots of trading interfaces; Text mode: operation logs (>1000 records / second); Time series mode: API call sequences (length >10,000).

[0270] Processing flow:

[0271] The API sequence is sliced ​​into windows (window 256) and converted into a grayscale image.

[0272] Visual features are extracted through the ViT-LSH encoder.

[0273] Dynamic gating assigns weights (0.6 for vision, 0.3 for text, and 0.1 for timing).

[0274] A spatial-temporal offset between the interface screenshot and the log operation is detected, triggering an alarm.

[0275] Effect: The false alarm rate is reduced to 1.2%, and the detection delay is less than 50ms.

[0276] Industrial IoT Intrusion Detection:

[0277] Modbus TCP protocol traffic is sliced ​​in layers (64×64 per layer).

[0278] Dynamic prompt generation: "Modbus [Function Code Abnormal] Request".

[0279] Performance: Feature extraction speed 153 frames / second (traditional method 52 frames / second).

[0280] Memory usage: 4.3GB (11.2GB for traditional solution).

[0281] This invention systematically solves the core problems in network behavior analysis, such as multimodal fragmentation, low computational efficiency, lack of long-range dependencies, and vulnerability to adversarial attacks, through four major technologies: cross-modal dynamic fusion architecture, hierarchical computing optimization, causal context modeling, and active adversarial defense. It provides a reliable feature extraction foundation for building the next generation of intelligent security systems.

[0282] The experimental index results of the traditional method and the solution of the present invention are compared, as shown in Table 1.

[0283] Table 1 Comparison of results

[0284]

[0285] The technical solution of this invention can be integrated into three types of products: the Next Generation Intelligent Security Gateway (NISG), the Cloud Security Monitoring Platform (CSMP), and the Industrial Edge Security Terminal (IEST). The following describes it from the perspectives of application scenarios, functional characteristics, and interactive design:

[0286] 1. Application scenarios and product positioning;

[0287] Table 2 Application scenarios and product positioning

[0288]

[0289] 2. Functional Features and Interaction Design

[0290] 1. Intelligent Security Gateway (NISG);

[0291] The NISG-Dashboard visualization platform supporting the present invention builds a multimodal network security decision-making center. Its interactive interface adopts a three-column layout. After logging in, users can execute a closed-loop path of screening by monitoring network segments - dynamic weight adjustment - real-time graph analysis - report export - policy deployment. The feature extraction rules and security policies are configured in a linked manner to ensure visual management and control of the entire process from multimodal traffic profiling to abnormal event handling, significantly improving the efficiency of tracing and responding to complex network security incidents.

[0292] 2. Security Monitoring Platform (CSMP);

[0293] The CSMP-Console multi-tenant security management platform, which supports this invention, builds an intelligent security operations hub. Its interactive interface adopts a layered layout and offers dual modes: one-click adoption and manual correction. In typical O&M scenarios, managers can quickly locate anomalous tenants through a sudden score drop alert (e.g., triggered by a 45-point threshold). After confirming high-risk operations through call chain backtracking, the system automatically recommends refined control strategies encompassing time, user, and action elements. This enables closed-loop control from behavioral profiling to policy deployment, significantly improving response accuracy and handling efficiency in multi-tenant environments.

[0294] 3. Industry Edge Terminal (IEST);

[0295] The IEST-Device industrial security terminal, designed for industrial control systems, features intelligent protection devices whose hardware interactions and functional modules are deeply integrated with industrial scenarios. Its operation process is highly automated: upon power-up, it automatically loads the latest feature library synchronized from the cloud, and upon connecting to the industrial network, it initiates deep protocol detection. When an abnormal operation triggers an alarm, managers can quickly locate specific details (such as illegal function code combinations) via the touchscreen and use the USB interface to export complete event reports containing timestamps and protocol fields. This creates a complete closed-loop process from protocol parsing and anomaly warning to evidence solidification, effectively enhancing the real-time defense of industrial control systems against unknown disturbances.

[0296] 3. Mapping of core technologies on the product side

[0297] Table 3 Mapping of core technologies on the product side

[0298]

[0299] The technical solution of the present invention realizes the explicit transformation of technical capabilities into user experience, transforming the complex multimodal feature extraction process into an operational and understandable interactive interface, significantly lowering the technical threshold for security operation and maintenance personnel.

[0300] As shown in Table 4 and Table 5, the hardware and software environments of the present invention are:

[0301] Table 4 Hardware environment

[0302]

[0303] Table 5 Software environment

[0304]

[0305] The front-end interaction logic of the system of the present invention is as follows Figure 2 As shown, the front end is responsible for user interaction and visual display.

[0306] RESTful API (Representational State Transfer Application Programming Interface) call path: / api / v1 / config / feature_weight.

[0307] In the request body example, "visual_weight": 0.6, "text_weight": 0.3, and "sequence_weight": 0.1.

[0308] The background processing logic of the system of the present invention: The microservice architecture adopts a modular design, and four core services implement high-performance RPC (Remote Procedure Call) communication through the gRPC framework, specifically:

[0309] 1) The Feature Extractor service is responsible for multimodal data preprocessing. It uses thread pool technology to parallelize visual, text, and time series data, completing data encoding and format unification.

[0310] 2) The Model Inference Engine is equipped with the CVOCA core model, supports batch inference with a batch size of 32, and implements end-to-end processing of dynamic optimization and contextual modeling.

[0311] 3) The Adversarial Trainer service generates adversarial samples daily based on the FGSM algorithm and continuously enhances system robustness through model fine-tuning.

[0312] 4) The Feature Store service builds a dual-mode knowledge base, maintaining a massive library of feature vectors and accumulating perturbation pattern templates. It integrates the Faiss vector search engine to enable millisecond-level nearest neighbor searches. Each service ensures independent scalability through a decoupled design. The inference service supports horizontal expansion to cope with traffic bursts, while the storage service leverages Faiss's quantization, compression, and index optimization technologies to efficiently manage tens of billions of feature data. The overall architecture forms a closed-loop processing chain: data preprocessing, model inference, security enhancement, and knowledge accumulation.

[0313] like Figure 5 As shown in the figure, the system adopts a three-layer distributed architecture to achieve full-scenario security monitoring: at the data center layer, server nodes implement full-traffic mirroring collection by accessing the SPAN port of the core switch, and are deeply integrated with the SIEM security information management system to complete functions such as traffic metadata extraction, abnormal alarm aggregation, and compliance auditing; lightweight edge devices are deployed at the industrial field layer, directly connecting to industrial controllers such as PLC / DCS, parsing industrial instructions through protocols such as Modbus / OPC UA, and using 4G / 5G networks to transmit pre-processed behavioral characteristics back to the cloud in real time, effectively solving the problem of wired deployment in industrial environments.

[0314] like Figure 6As shown in the figure, after the user configures feature weights through the front-end interface, the front-end sends the configuration information to the / api / config interface on the back-end via a POST request. Upon receiving the request, the back-end starts the Feature Extractor to collect data. After the Feature Extractor completes data collection, it requests batch inference from the Inference Engine. The Inference Engine then obtains the adversarial model from the Adversarial Trainer and stores the inferred feature vectors in the Feature Store. The Feature Store pushes the real-time feature stream to the front-end, which then updates the abnormal event map and displays it to the user.

[0315] Through the innovative CVOCA multimodal feature extraction architecture, this invention systematically solves the core pain points of existing network behavior analysis technology in multimodal data fusion, computational efficiency, adversarial robustness and dynamic adaptability, significantly improving technical performance and application value.

[0316] Example 2

[0317] The core goal of this invention is to solve the problems of multimodal data fusion, computational efficiency, adversarial robustness and dynamic adaptability in network behavior analysis through steps such as cross-modal unified encoding, dynamic feature optimization, context-aware modeling and adversarial robustness enhancement.

[0318] The difference between this embodiment and embodiment 1 is that this embodiment can list multiple alternative implementation methods and scalable application scenarios for the key modules in embodiment 1. These alternative solutions can achieve the purpose of the present invention and provide differentiated options in terms of technical paths or application fields. Specifically:

[0319] 1. Alternative implementation of cross-modal unified encoding

[0320] Main solution: Use ViT (visual encoder) and BERT (text encoder) combined with contrastive learning to achieve multimodal data alignment.

[0321] Alternatives:

[0322] 1. CLIP model variant;

[0323] Implementation: Directly use the CLIP (Contrastive Language-Image Pretraining) model and use pre-trained image-text alignment capabilities to process multimodal data.

[0324] Features: CLIP has been pre-trained on large-scale data, and transfer learning can quickly adapt to network behavior analysis scenarios, simplifying the training process.

[0325] 2. ALIGN architecture;

[0326] Implementation: Use the ALIGN model, combined with EfficientNet (vision) and BERT (text) encoders, to align modal features through large-scale noisy data.

[0327] Features: More robust to noisy data such as unstructured network logs, and adaptable to complex input scenarios.

[0328] 3. ResNet + RoBERTa combination;

[0329] Implementation: ResNet-50 is used to encode visual data, RoBERTa (Robustly Optimized BERTPretraining Approach) is used to encode text data, and feature alignment is achieved through a shared projection layer.

[0330] Features: ResNet has high computational efficiency, and RoBERTa excels at processing long texts and is suitable for resource-constrained environments.

[0331] 4. Multimodal Graph Neural Network (GNN);

[0332] Implementation method: Model different modal data as graph nodes, the connections between modalities as edges, and use GNN to extract cross-modal features.

[0333] Features: Explicitly modeling the relationship between modalities and enhancing the ability to extract semantic associations.

[0334] 2. Alternative Implementation Methods for Dynamic Feature Optimization

[0335] Main solution: Use a differentiable gating mechanism to dynamically adjust the weights of multimodal features.

[0336] Alternatives:

[0337] 1. Attention mechanism fusion;

[0338] Implementation: Use a multi-head attention mechanism to calculate the correlation between modal features and automatically generate fusion weights.

[0339] Features: Capture fine-grained interaction relationships and improve feature fusion accuracy.

[0340] 2. Adaptive weighting strategy;

[0341] Implementation: Calculate uncertainty based on the entropy or variance of modal data and dynamically assign weights (modals with high uncertainty have lower weights).

[0342] Features: No additional network layer is required, and the calculation is more efficient.

[0343] 3. Reinforcement learning scheduling;

[0344] Implementation: Train a reinforcement learning agent to dynamically select modality combinations based on device load or data distribution.

[0345] Features: Adaptive optimization of long-term performance, suitable for dynamic environments.

[0346] 4. Bayesian feature fusion;

[0347] Implementation: Use Bayesian networks to model conditional dependencies between modalities and perform probability-weighted fusion.

[0348] Features: Provide uncertainty estimation and enhance decision robustness.

[0349] 3. Alternative Implementations of Context-Aware Modeling

[0350] Main solution: Use the Transformer architecture to capture long-range temporal dependencies.

[0351] Alternatives:

[0352] 1. RNN / LSTM network;

[0353] Implementation: Use bidirectional LSTM to process time series features and capture context dependencies.

[0354] Features: high computing efficiency, low memory usage, suitable for short time series data.

[0355] 2. Temporal Convolutional Network (TCN);

[0356] Implementation: Use dilated convolution to model long-term dependencies and reduce the number of parameters.

[0357] Features: Fast inference speed, suitable for edge device deployment.

[0358] 3. Graph Convolutional Network (GCN);

[0359] Implementation: Model the network conversation as a graph structure, and GCN extracts the temporal correlation between nodes.

[0360] Features: Explicitly modeling session jump relationships to improve path recognition capabilities.

[0361] 4. Causal Convolutional Network;

[0362] Implementation: Use causal convolution to ensure that feature extraction complies with temporal logic.

[0363] Features: low latency, suitable for real-time detection scenarios.

[0364] 4. Alternative Implementation Methods for Enhanced Robustness

[0365] Main solution: Use gradient reversal layer (GRL) and adversarial distillation training to enhance robustness.

[0366] Alternatives:

[0367] 1. Virtual Adversarial Training (VAT);

[0368] Implementation: Generate virtual perturbations in the feature space to optimize model smoothness.

[0369] Features: No labels required, high training flexibility.

[0370] 2. Generative Adversarial Networks (GANs);

[0371] Implementation: Train the generator to generate adversarial samples, and the discriminator to optimize feature robustness.

[0372] Features: Generate diverse adversarial samples and improve defense breadth.

[0373] 3. Feature space regularization;

[0374] Implementation method: Introduce Lipschitz constraints to limit the feature gradient norm.

[0375] Features: Theoretically guarantees feature smoothness and stable defense effect.

[0376] 4. Self-supervised adversarial learning;

[0377] Implementation: Enhance the model's robustness to local perturbations through the mask reconstruction task.

[0378] Features: No additional adversarial samples are required, and the training cost is low.

[0379] 5. Other application scenarios

[0380] Main scenario: network behavior analysis (such as security gateway, cloud monitoring, and industrial edge).

[0381] Extended scenario:

[0382] 1. Medical image analysis;

[0383] Application method: Fusion of CT images (visual), medical records (text), and physiological signals (time series).

[0384] Effect: Improve the accuracy of multimodal diagnosis and reduce misdiagnosis.

[0385] 2. Financial risk control;

[0386] Application method: Integration of transaction data (time series), user behavior logs (text), and market news (text).

[0387] Effect: Identify abnormal transactions in real time and reduce financial risks.

[0388] 3. Autonomous driving;

[0389] Application method: Integrate on-board cameras (vision), lidar (point cloud), and navigation instructions (text) to achieve environmental perception.

[0390] Effect: Improve the accuracy of multi-source data fusion and enhance driving safety.

[0391] 4. Intelligent customer service;

[0392] Application method: Integrate user voice (audio), conversation text (text), and emotional images (vision) to achieve intent recognition.

[0393] Effect: Improve response accuracy and optimize user experience.

[0394] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A CVOCA feature extraction method based on a multimodal large model, characterized by: The following steps are involved: S1: Data input, including network traffic data, log text data, and system call sequences; S2: Data preprocessing: processing network traffic data, log text data, and system call sequences to generate standardized input; S3: Unified feature representation, using a cross-modal unified encoder to map visual, textual, and temporal data into a unified feature space; S4: Feature optimization, based on a dynamic feature optimizer, dynamically adjusts modal weights and achieves efficient feature fusion; S5: Context-aware modeling, using causal Transformer combined with local attention windows to capture long-range temporal dependencies; S6: Adversarial optimization, improving model robustness through adversarial training; S7: Feature storage and update system, which uses expert network and gating mechanism to realize dynamic update of feature library.

2. The CVOCA feature extraction method based on a multimodal large model according to claim 1, characterized in that: In S2, the specific preprocessing contents include: S21: Network traffic visualization: Slice the original traffic data into time windows and convert them into grayscale spectrograms; S22: Log text structuring: using regular expressions to extract key fields; S23: Constructing a protocol-behavior keyword dictionary: annotating the regular matching extracted fields with protocol keywords; S24: System call sequence encoding: mapping the system call ID to a 32-dimensional word vector; Generate time series feature blocks through sliding windows.

3. The CVOCA feature extraction method based on a multimodal large model according to claim 1 is characterized in that: The specific process of S3 is: S31: Visual coding, using the improved ViT-B / 32 model, adding a local sensitive hashing module to the embedding layer to enhance the local features of the traffic spectrum graph; S32: Text encoding, based on the BERT architecture, introduces dynamic position encoding; S33: Contrastive learning alignment, setting a two-stream contrastive loss function to align visual and text feature spaces; S34: Temporal encoding, processed using a Transformer encoder, converts each system call word vector in the sequence into a context-aware temporal feature vector.

4. The CVOCA feature extraction method based on a multimodal large model according to claim 1, characterized in that: The specific process of S4 is: S41: Introducing a differentiable gating mechanism to calculate modality importance weights based on the MLP network; S42: dynamic feature fusion; S43: Resource-aware scheduling: Dynamically adjust the feature slice size based on the real-time status of the device's video memory.

5. The CVOCA feature extraction method based on a multimodal large model according to claim 1 is characterized in that: The specific process of S5 is as follows: S51: Causal Transformer Architecture: Setting up multi-head attention with causal masks; S52: Session-level state tracking: Use LSTM to maintain the session state vector.

6. The CVOCA feature extraction method based on a multimodal large model according to claim 1, characterized in that: The specific process of S6 is as follows: S61: Adversarial example generation; S62: Calculate the adversarial loss based on the FGSM algorithm.

7. The CVOCA feature extraction method based on a multimodal large model according to claim 1, characterized in that: The specific process of S7 is as follows: MoE incremental learning: Setting up a mixture of experts system and calculating each expert’s focus on a specific disturbance type; Feature version management: Feature update records are stored based on blockchain to ensure traceability.

8. A CVOCA feature extraction system based on a multimodal large model, implementing the CVOCA feature extraction method based on a multimodal large model according to any one of claims 1 to 7, characterized in that: include: Multimodal input preprocessing module: used to standardize the input heterogeneous data; Cross-modal unified encoder: used to map multi-source information into a shared semantic space and complete the initial modeling of semantic associations between modalities; Dynamic feature optimizer: Adaptive weight adjustment and redundancy suppression of preliminary features based on real-time feedback mechanism; Context-aware modeling module: used to integrate temporal correlation and scene dependency information to construct high-order feature representations with logical coherence; Adversarial robustness enhancement module: Improves feature anti-interference capabilities through adversarial training and noise injection strategies; Feature storage and update system: used to achieve incremental accumulation and version management of feature knowledge, forming a continuously evolving multimodal feature knowledge base.

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