Intrusion Detection for Network Based on Elite Clone Artificial Bee Colony and Back Propagation Neural Network

Abstract

With the rapid development of Internet technology, network attacks have become more frequent and complex, and intrusion detection has also played an increasingly important role in network security. Intrusion detection is real-time and proactive, and it is an indispensable technology under the diversified trend of network security issues. In terms of network security, neural networks have the characteristics of self-learning, self-adaptation, and parallel computing, which are very important in intrusion detection. This paper combines back propagation neural network (BPNN) and elite clone artificial bee colony (ECABC) to propose a new ECABC-BPNN, which updates and optimizes the settings of traditional BPNN weights and thresholds. Then, apply ECABC-BPNN to network intrusion detection. Use the attack data samples of KDD CUP 99 and water pipe for attack classification experiments using GA-BPNN, PSO-BPNN, and ECABC-BPNN. The results show that the ECABC-BPNN proposed in this paper has an accuracy rate of 98.08% on KDD 99 and 99.76% on water pipe data. ECABC-BPNN effectively improves the accuracy of network intrusion classification and reduces classification errors. In addition, the time complexity of using ECABC-BPNN to classify network attacks is relatively low. Therefore, ECABC-BPNN has superior performance in network intrusion detection and classification.