8 results about "Traffic generation model" patented technology

The embodiment of the invention relates to the field of data processing, and provides an attack path identification method of a complex network and a related device, and the method comprises the steps: obtaining network historical traffic data; according to the network historical traffic data, adjusting a CNN neural network model to obtain a network simulation traffic generation model; generating network simulation attack traffic data by adopting a network simulation traffic generation model; determining an attack path graph model according to the generated network simulation attack traffic data; and identifying the real-time network traffic according to the attack path graph model, and generating a network traffic attack path identification result, so that a novel attack mode of the network traffic can be effectively identified in combination with the network traffic attack path identification result generated by the attack path graph model, and the identification precision of network attacks is improved.

The invention discloses a satellite network-oriented ground service flow generation simulation method, and belongs to the technical field of communication. According to the method, by simulating generation factors, including geographic areas, population, time and other multi-dimensional factors, influencing ground satellite communication services, flow generation models of different levels are input according to different requirements, and finally area flow information is generated, so that the requirement of simulating satellite network input is met. Geography-population distribution information is utilized, flexible region merging is adopted, and region flow simulation models of different levels can be flexibly set according to simulation requirements, so that the simulation accuracy is ensured, and meanwhile, the ground service flow generation simulation complexity is reduced.

The invention discloses a network flow generating and repairing method based on protocol constraint. The method comprises the following steps: analyzing an original traffic capture file, extracting basic features through stream-level recombination and session segmentation, and mapping a continuous time sequence and a discrete protocol field to a uniform feature space; and on the basis, constructing a diffusion type network flow generation model to capture a statistical attribute and time sequence dependency relationship of the network flow. In the generating or repairing process, a protocol state machine is constructed, protocol constraint is introduced in the reverse denoising stage, the sampling process is constrained through a legality guiding mechanism, and protocol logic violation in the generating process is avoided. And finally, through feature inverse mapping and virtual protocol stack state maintenance, reconstructing to obtain network flow data which meets a communication protocol specification and can be correctly analyzed by a real protocol stack. According to the method, the generation and repair of the network traffic can be realized under a unified framework, and the correctness of a result in protocol semantics is ensured.

This invention discloses a data link network traffic generation method based on Generative Adversarial Networks (GAI), belonging to the field of data link network technology. This invention can utilize a GAI model to learn the distribution and behavioral characteristics of historical traffic data, generating comprehensive traffic that conforms to real network characteristics. This framework supports unlimited traffic generation without deploying real network services. This invention employs an improved IP2Vec+CGAN network model for data link network traffic generation, introducing an embedding-based method that uses continuous vectors to represent flow quintuples. A comprehensive network traffic generation model based on CGAN is proposed. We use the vector representation of flow quintuples, statistical characteristics of network traffic, and traffic types to train the CGAN model. After traffic generation, it is transformed back from the generated state to its original space, which is then used to describe network behavior between IoT devices or users.

The invention belongs to the technical field of traffic generation, and discloses a diversity-oriented industrial protocol format inference traffic generation method. Designing an adversarial flow generation model oriented to format inference, and isolating learning of flexible payload distribution from rigid syntactic enforcement by adopting a decoupled generation architecture; the generation process is separated from a deterministic rule, so that the traffic generation model can synthesize a high-entropy payload mode exceeding the finite diversity of an original sparse data set; by isolating flexible distributed learning from rigid syntactic enforcement, the FIGAN solves the internal conflict between syntactic effectiveness and semantic diversity, and a new normal form is provided for industrial traffic enhancement.

The present application discloses a link-level traffic prediction method and device for a 6G heterogeneous aggregation network, the method comprising: based on a preset test environment, using a preset 6G traffic generation model to generate a 6G link-level user traffic data set by link allocation; using the 6G link-level user traffic data set to perform traffic prediction training on the initial traffic prediction model to obtain a 6G user traffic prediction model; inputting the historical link-level user traffic data of the target user into the 6G user traffic prediction model to perform future traffic prediction to obtain a target traffic prediction result. Generating user traffic data by using a preset 6G traffic generation model can provide more reliable analysis data; using a traffic prediction model for prediction can ensure the accuracy of the prediction results. Therefore, the present application can solve the technical problem that the prior art cannot simultaneously capture the spatiotemporal characteristics of user traffic, and the data construction method is not suitable for 6G heterogeneous aggregation networks, resulting in a lack of accuracy in the prediction results.

The present disclosure provides a method, device and electronic device for generating malicious attack traffic, which can identify the attack behavior of historical malicious attack data and construct an attack data interaction graph; extract the attack behavior chain and adjust the attack behavior parameters of the preset malicious attack traffic generation model; adjust the attack attribute parameters of the preset malicious attack traffic generation model according to the defense response strategy and defense response effect of the historical malicious attack data; perform multimodal fusion on the historical malicious attack data from different data sources, and train the preset malicious attack traffic generation model under the guidance of the attack behavior parameters and attack attribute parameters according to the fused historical malicious attack data; input the target timestamp and the target attack condition into the trained preset malicious attack traffic generation model to generate malicious attack traffic data with the target timestamp. The generation efficiency of malicious encrypted traffic can be improved, and the data diversity and authenticity can be improved at the same time.

The application discloses a method for constructing an enhanced network threat detection model based on adversarial learning, comprising the steps of constructing a network threat detection model, constructing a threat traffic generation model, and combining the trained threat traffic generation model and the network threat detection model to obtain an enhanced network threat detection model. The application has the following beneficial effects: 1) for the emerging variant adversarial attack threat, the network threat detection model is enhanced through the adversarial training method, and the network threat detection model can better adapt to the real network environment; 2) based on WGAN, the variant attack traffic sample is generated, and the traffic sample closest to the to-be-detected traffic is screened out and replaced, so that the sample can meet the existing traffic distribution and can avoid the escape risk caused by the variant attack sample; 3) the black-box adversarial attack can be effectively coped with without assuming the variant adversarial path in advance.