Method and device for identifying abnormal traffic of Internet of Vehicles based on instruction sequence
A technology of abnormal traffic and instruction sequence, applied in the direction of secure communication device, neural learning method, biological neural network model, etc., can solve the problem of single input object of protocol specification
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Embodiment 1
[0080] Such as Figure 1-2 As shown, a method for identifying abnormal traffic in the Internet of Vehicles based on instruction sequences includes the following steps:
[0081] S1. Data traffic collection and analysis for training: The traffic collection module captures the normal traffic and abnormal traffic in the Internet of Vehicles traffic through the packet capture tool and outputs them as training data traffic to the data preprocessing module. The normal traffic is the interaction between the vehicle terminal and the cloud service platform Traffic, abnormal traffic is collected from the cloud service platform;
[0082] Normal traffic includes registration traffic, authentication traffic, heartbeat traffic, map traffic query traffic, upload traffic, assisted driving information traffic, and entertainment information service traffic. Abnormal traffic includes intrusion traffic, scanning detection traffic, and DDOS traffic collected by the cloud service platform. Both tra...
Embodiment 2
[0102] Such as Figure 1-2 As shown, a method for identifying abnormal traffic in the Internet of Vehicles based on instruction sequences includes the following steps:
[0103] 1. Data traffic collection and analysis, mainly to capture the interactive data generated during the communication process of the Internet of Vehicles, including the collection of interactive traffic information between the vehicle terminal and the cloud service platform. The abnormal traffic is mainly the intrusion and detection traffic collected on the cloud service platform;
[0104] 2. Vehicle networking traffic preprocessing, split the mixed traffic into multiple groups of session flows with different source IPs through triplets (source IP, destination IP, destination port);
[0105] 3. Sequentially divide the data streams of different source IPs into single streams according to the quintuple (source IP, source port, destination IP, destination port, transport layer protocol), and extract them at t...
Embodiment 3
[0130] Such as image 3 As shown, a device for identifying abnormal traffic in the Internet of Vehicles, including a traffic collection module, a data preprocessing module, a rule extraction module and a model training module connected in sequence;
[0131] The traffic acquisition module is used to capture the normal traffic and abnormal traffic in the Internet of Vehicles traffic through the packet capture tool and output it to the data preprocessing module as the data traffic for training;
[0132] Normal traffic includes registration traffic, authentication traffic, heartbeat traffic, map traffic query traffic, upload traffic, assisted driving information traffic, and entertainment information service traffic, and abnormal traffic includes intrusion traffic collected by the cloud service platform, scanning detection traffic, and DDOS traffic;
[0133] The data preprocessing module is used to receive training data traffic and the Internet of Vehicles traffic to be detected, ...
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