Although the anonymous communication network Tor can protect the security of users’ data and privacy during their visits to the Internet, it also facilitates illegal users to access illegal websites. Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations. Existing methods tend to manually extract the traffic features of users visiting websites and construct machine learning or deep learning models to classify the features. While these methods can be effective in classifying unknown website traffic, the effect of classification in the use of defensive measures or onion service scenarios is not yet ideal. This paper proposes a method to identify Tor users visiting websites based on frequency domain fingerprinting of network traffic (FDF). We extract the direction and length features of circuit sequences in access traffic and combine and transform them into the frequency domain. The classification of access traffic is accomplished by using a deep learning classification model combining CNN, FC, and Self-Attention. In this paper, the proposed FDF method is experimentally validated in common scenarios of Tor networks. The results show that FDF outperforms the existing methods for classification in different Tor scenarios. It can achieve 98.8% and 94.3% classification accuracy in undefended and WTF-PAD defense scenarios, respectively. In the onion service scenario, the accuracy is improved by 4.7% over the current state-of-the-art Tik-Tok method.
The optimal reconfiguration of the distribution network doesn’t require any cost and becomes an effective technique for reducing power loss. So this paper proposes an efficient cuckoo search algorithm for this problem. First, the mapping relationship between the cuckoo search algorithm and the distribution network is established; secondly, the algorithm generates the initial population based on the set of feasible solutions; finally, algorithm updates the nest position by the Lévy flight and nest parasitic behavior. A variable domain search algorithm is introduced to improve for the shortcomings of the cuckoo search algorithm in the process of solving the distribution network reconfiguration problem. Finally, it is verified that the algorithm has the ability to quickly solve the optimal solution by the IEEE33 model simulation analysis.
In this paper, the object of study is secure transmission and green energy transfer in full-duplex (FD) wireless-powered relay (WPR) secure systems, where an FD relay collects the power from radio-frequency signs and transmits the information in the face of multiple eavesdroppers. In order to improve the efficiency and safety of the contemporaneous wireless energy and information delivery, we propose a joint energy-signal- (ES-) aided secure beamforming and time-switch scheme under the self-power circulation protocol at the relay. The question formulated in this paper is to maximize the confidentiality rate according to energy restrictions at both the relay and energy receiver. As the question is non-salient and hard to resolve directly, we transform it into two sub-problems. For the first sub-problem, a two-level optimization technique is suggested to separately gain the optimal beamforming as well as the ES covariance. The extrinsic rank is a single-variable majorization question, which can be solved by single-dimensional (1D) examination. We attain an optimal solution to the inner level by a semi-definite relaxation (SDR) technique. For the second sub-problem, we again use 1D search to solve this problem. Moreover, we prove that SDR always exists as a level-1 optimal resolution. Mathematical outcomes show that the suggested plan can achieve a considerable gain of confidentiality rate by comparison with other benchmark plans.
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