Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

• Published in: PloS one Vol. 10; no. 3; p. e0120976
• Main Authors: Wu, Jianfa, Peng, Dahao, Li, Zhuping, Zhao, Li, Ling, Huanzhang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.03.2015; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Classification; Cluster Analysis; Clustering; Computer science; Computer security; Convergence; Cybersecurity; Data mining; Defects; Elitism; Engineering; Fault diagnosis; General regression neural networks; Genetic algorithms; Intrusion; Intrusion detection systems; Methods; Network security software; Neural networks; Neural Networks, Computer; Optimization; Optimization theory; Particle swarm optimization; Principal Component Analysis; Principal components analysis; Regression analysis; Run time (computers); Swarm intelligence; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0120976

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.