Tor, as an anonymous communication network tool, presents challenges in terms of identification due to its anonymous and multi-encrypted characteristics. The advent of deep learning has provided new solutions for identifying this type of traffic. However, in practical scenarios, sharing large amounts of traffic data is often not feasible. To address this issue, this paper proposes a Tor traffic identification scheme based on federated learning. By processing distributed data on federated learning clients, we can extract bidirectional temporal information from the traffic and perform model training. Utilizing federated learning, we can aggregate model parameters and achieve distributed identification and classification of Tor traffic. Experimental evaluation was conducted using a publicly available dataset to validate the effectiveness of our proposed approach.
In order to strengthen the power network structure of our country, reduce the frequency of electrical equipment and lines, so that the power network can operate normally and smoothly. Based on the above reasons, the flow feature association mining algorithm is introduced into the research of power network security situation assessment to evaluate and predict the safe running state of power network. Firstly, this paper explains the basic algorithm of stream feature mining, including time correlation, IP (Ingress Protection) correlation, type correlation and super warning correlation. Secondly, it is necessary to determine the weight of network security evaluation index and the calculation method of evaluation, and carry out the relevant case analysis. Finally, the network security situation assessment model based on the flow feature association mining algorithm was compared with the BP(Back Propagation) neural network and RBF(Radial Basis Function) neural network evaluation model, and the comparison results showed that Flow feature association mining algorithm can improve the feasibility and accuracy of power network situation assessment. In conclusion, the study of this paper can provide a new research approach for the construction of network security situation assessment.
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