title: Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.
abstract: 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.
authors: College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China.
authors: Jianfa
authors: J
authors: Wu
authors: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors: Dahao
authors: D
authors: Peng
authors: College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
authors: Zhuping
authors: Z
authors: Li
authors: College of National Secrecy, Harbin Engineering University, Harbin, Heilongjiang, China.
authors: Li
authors: L
authors: Zhao
authors: College of Science, Harbin Engineering University, Harbin, Heilongjiang, China.
authors: Huanzhang
authors: H
authors: Ling
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016571:Neural Networks, Computer; D025341:Principal Component Analysis