科研成果详情

With the development of technology, wireless sensor networks (WSNs) has been widely used in military, political, medical and other fields, their characteristics of data-centric become increasingly prominent. In this paper, a data-oriented intruding detection method based on chaos theoy is proposed. We use the theory of chaotic system to analyze the internal rules of the sensory data and predict the data by RBF neural network firstly, then make an initial detection of false injected data attack according to whether the difference between the predicted and actual value is more than the threshold, finally confirming the attack by checking whether the number of abnormal within the cycle lies in the corresponding range. Experimental results show that RBF neural network predict sensory data more accurate, our approach can effectively distinguish the abnormal events caused by the attack or environmental factors and has high intrusion detection accuracy