Using particle swarm optimization in fuzzy association rules‐based feature selection and fuzzy ARTMAP‐based attack recognition

Abstract

Feature selection is a classic research topic in data mining, and it has attracted much interest in many fields such as network security. In addition, data mining approaches such as fuzzy association rule mining (FARM) can improve the performance of intrusion detection systems. In this study, a FARM‐based feature selector is proposed in order to reduce the dimension of input features to the misuse detector. Furthermore, a fuzzy ARTMAP neural network is used as the classifier. The accuracy of the proposed approach depends strongly on the precision of the parameters of FARM‐based feature selector module and fuzzy ARTMAP neural classifier. Particle swarm optimization (PSO) algorithm is incorporated into the proposed method to determine optimum values of parameters. In this way, the performance of PSO algorithm is compared with genetic algorithm (GA), as well. Experimental results indicate that PSO outperforms GA both in population size and number of evolutions and can converge faster. This is very important for enhancing the mining performance in large datasets such as intrusion detection datasets. When compared with some other machine learning methods, the proposed system indicates better performance in terms of detection rate, false alarm rate, and cost per example. Copyright © 2012 John Wiley & Sons, Ltd.

Trust dynamic task allocation algorithm with Nash equilibrium for heterogeneous wireless sensor network

“…Compared with the other evolutionary algorithms, PSO has the following strengths: ease of implementation, high‐quality solutions, computational efficiency and speed of convergence. It has been applied to solve optimization problems in many areas successfully . In a WSN, the issues such as node deployment, localization, energy‐efficient clustering, data fusion and topology control are often formulated as optimization problems, and PSO has been applied to address these WSN issues.…”

Comparative Study of Different Models before Feature Selection and AFTER Feature Selection for Intrusion Detection

“…Intrusion may affect the integrity, availability of the computer recourse. Intrusion may be external attacks or internal attacks [2]. The process of detecting and preventing intrusion activity is known as intrusion detection.…”