Affiliations: [a]
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| [b] State Key Laboratory of Software Architecture, Neusoft Corporation, Shenyang, China
| [c]
Research Center of Safety Engineering Technology in Industrial Control, Neusoft Group Research, Liaoning Province, Shenyang, China
| [d]
Engineering Research Center of the Ministry of Education in Security Protection for Complex Networks and Systems, Northeastern University, Shenyang, Liaoning, China
| [e] Key Laboratory of Medical Image Computing, Ministry of Education, Northeastern University, Shenyang, China
Abstract: Wireless Sensor Networks (WSNs) are vulnerable to various localization attacks where attackers intended to provide improper beacons or manipulate the location determination. Attack classification for localization in WSNs is not only the condition, prerequisite and premise of threat analysis, but, more significantly, a vital part of the security anomaly detection. In this paper, a localization attack recognition method using a deep learning architecture was proposed. To enhance the classification performance, a good feature representation was established through combining location features with topological indexes based on the complex network theory. The ability of Stacked Denoising Autoencoder (SDA) to learn the underlying features from input data was exploited. Back-propagation algorithm was performed to update weights through a stochastic gradient descent method. The proposed approach could efficiently distinguish the Sybil attacks, Replay attacks, Interference attacks, Collusion attacks and normal beacons. Extensive experiments demonstrated that the proposed algorithm can achieve an average classification accuracy of 94.39% and was more robust and efficient even in the existent of huge baneful beacons.
Keywords: Security, wireless sensor networks, attack classification, deep learning
