Feedforward neural network based on ensemble evolutionary algorithm with self-adaptive strategy and parameter for intrusion detection. (29th July 2019)

• Record Type: Journal Article
• Authors: Xue, Yu; Tang, Tao; Liu, Alex X.
• Is Part Of: International journal of wireless and mobile computing. Volume 17:Number 2(2019)
• Page Start: 202
• Page End: 211
• Publication Date: 2019-07-29
• Subjects: WSN -- wireless sensor network -- intrusion detection -- FNN -- feedforward neural network -- classification -- evolutionary computation -- particle swarm optimisation -- differential evolution -- self-adaptive strategy -- parameter; Mobile computing -- Periodicals; Wireless communication systems -- Periodicals; 004.6
• Languages: English
• ISSNs: 1741-1084

Abstract

The applications of Wireless Sensor Networks (WSNs) have driven the development of many fields, and the information security is an important issue in the WSNs. Intrusion Detection System (IDS) is a kind of effective system for identifying network attacks and protecting data security, which has a great significance for WSNs. In this paper, we transform the intrusion detection problem into a classification problem and introduce the Feedforward Neural Network (FNN) classifier to solve it. At the same time, we propose a novel Self-adaptive Parameter and Strategy Differential Particle Swarm (SPS-DPS) optimisation algorithm to find the optimal weights for the FNN. Experiments are performed on eight data sets which are constructed based on the intrusion detection data set KDDCUP99, and the results are compared with five Evolutionary Computation (EC) methods. The results show that the SPS-DPS based FNN method can solve the intrusion detection problem well compared with the other methods.