Source location privacy, one of the core contents of Wireless Sensor Network (WSN) security, has a significant impact on extensive application of WSNs. In this paper, a novel location privacy protection routing scheme called Energy Balanced Branch Tree (EBBT) is proposed by using multibranch and fake sources. This scheme has three phases. In the first place, the data of the source are randomly sent to a certain intermediate node. Then, a minimum hop routing (MHR) from the intermediate node to the base station is formed. Then, branch paths with fake sources are generated dynamically from some nodes on the MHR path. Finally, a tree-shaped structure from real source nodes and fake source nodes to the base station is achieved. In difference to the previous schemes, the location of the real source in the EBBT scheme does not affect the location and the number of fake sources. During the formation of the tree-shaped multibranch paths, the residual energy of nodes is considered sufficiently, and the control of the direction of each branch path is also involved. The influence of the number and length of branches on the network lifetime and network security is also investigated. Experimental results show that the proposed algorithm has the advantages of long network security period and lifetime, as well as high path diversity. Our simulation further illustrates that the EBBT scheme has favorable privacy of the source location without changing the network lifetime.
Currently, deep learning has provided an important means to solve problems in various fields. Intelligent computing will bring a new solution to the security analysis of lightweight block cipher as its analysis becomes more and more intelligent and automatic. In this study, the novel multiple differential distinguishers of round-reduced SIMECK32/64 based on deep learning are proposed. Two kinds of SIMECK32/64’s 6–11 rounds deep learning distinguishers are designed by using the neural network to simulate the case of the multiple input differences and multiple output differences in multiple differential cryptanalysis. The general models of the two distinguishers and the neural network structures are presented. The random multiple ciphertext pairs and the associated multiple ciphertext pairs are exploited as the input of the model. The generation method of the data set is given. The performance of the two proposed distinguishers is compared. The experimental results confirm that the proposed distinguishers have higher accuracy and rounds than the distinguisher with a single difference. The relationship between the quantity of multiple differences and the performance of the distinguishers is also verified. The differential distinguisher based on deep learning needs less time complexity and data complexity than the traditional distinguisher. The accuracy of filtering error ciphertext of our 8-round neural distinguisher is up to 96.10%.
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