Distributed localization attack type recognition algorithm for malicious nodes in wireless sensor networks

The process of localization in wireless sensor networks is easily attacked by malicious nodes. In order to identify the types of those external attacks, an Alternating Direction Method of Multipliers-p-Norm Support Vector Machines(ADM-PSVM) algorithm is proposed. The proposed algorithm is based on classification model of the linear support vector machine. Firstly, by introducing a norm constraint into the classification algorithm, the adaptability of classifier for various types of dataset can be enhanced via selecting different value p. Then we derive distributed form of the algorithm according to Alternating Direction Method of Multipliers; this makes the classifier have the ability to distribute computing task among different nodes based on the residual energy of each node. Finally, the sample and testing dataset for each of four types of external malicious nodes are implemented in the training and testing processes of the proposed algorithm, and the influence on recognition accuracy performance in various p values and penalty factor η ones are discussed. The experimental results show that the proposed algorithm can achieve higher classification accuracy and better computational efficiency on the hostile external attack dataset.