Network Sentence Embeddings for Anomaly Detection
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Solution Overview
Problem
Current network security systems face challenges in accurately distinguishing between outliers and valid anomalies in network data, particularly due to high data volumes and privacy concerns, which limit the effectiveness of traditional analysis methods.
Innovation Solution
The use of Natural Language Processing (NLP) techniques, specifically sentence embeddings, to represent network interactions as semantic structures, allowing for the analysis of contextual relationships and equivalencies in network activity, thereby enhancing the accuracy of network security analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security analysis methods are used, then data volume processing capability is limited, but measurement precision of anomalies deteriorates
Solution Approach 1:
The patent extracts only the essential semantic features from network data using sentence embeddings, transforming high-dimensional network interaction data into compressed semantic representations. This extraction process retains the critical information needed for anomaly detection while discarding redundant data, thereby improving measurement precision without being overwhelmed by data volume.
Solution Approach 2:
The patent changes the parameter representation of network data by converting raw network interactions into sentence embedding vectors that capture semantic meaning. This parameter transformation enables more effective anomaly detection by representing network data in a format that preserves contextual relationships while reducing dimensional complexity.
2Measurement precision
If context-based analysis is implemented, then anomaly detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces sentence embeddings as an intermediary layer between raw network data and anomaly detection algorithms. This intermediary transforms complex network interactions into standardized semantic representations, simplifying the subsequent analysis process while preserving contextual information needed for accurate anomaly detection.
Solution Approach 2:
The patent replaces traditional mechanical analysis methods with NLP-based semantic analysis. By substituting conventional network analysis approaches with sentence embedding techniques, the system achieves better contextual understanding while managing complexity through established NLP frameworks.
3Adaptability or versatility
If semantic equivalence is established across diverse networks, then adaptability is improved, but measurement precision of equivalency deteriorates
Solution Approach 1:
The patent creates a universal semantic representation framework using sentence embeddings that can process network data from diverse architectures and protocols. This universal approach allows the system to adapt to different network types while maintaining consistent anomaly detection capabilities across heterogeneous environments.
Solution Approach 2:
The patent transforms diverse network parameters into a unified semantic space through sentence embeddings. By changing the representation parameters of different network types into a common embedding format, the system achieves cross-network adaptability while preserving the essential characteristics needed for accurate equivalency detection.
Data Source
AI summary
Systems and methods are provided for utilizing natural language process (NLP), namely semantic learning approaches in network security. Techniques include analyzing network transaction records to form a corpus related to a semantics of network activity. The corpus includes formulated network sentences, representing sequences of network entities that are accessed in the network. A corpus of network sentences can include sequences of servers accessed by each user. A network sentence embeddings model can be trained on the corpus. The network sentence embeddings model includes an embedding space of text that captures the semantic meanings of the network sentences. In sentence embeddings, network sentences with equivalent semantic meanings are co-located in the embeddings space. Further, proximity measures in the embedding space can be used to identify whether network sentences (e.g., access sequences), are semantically equivalent. Using network sentence embeddings model, equivalent semantics of access can be established to efficiently detect anomalies.