Abstract
Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.
Highlights
Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks
It explains about a new India systems (IDS) which has been developed using two proposed algorithms namely intelligent rule-based attribute selection algorithm and intelligent rule-based enhanced multiclass support vector machine (IREMSVM)
Enhanced multiclass support vector machine In the IREMSVM algorithm, the data set is first divided into R classes
Summary
They proposed an optimal algorithm to solve this problem, and based on that, they classified the attacks effectively. The least square support vector machine (LSSVM) is a modified algorithm [78] to the standard SVM It solves a linear equation in the optimization stage and simplifies the process. This LSSVM is effective since it avoids local minima in SVM problems used by LSSVM classifier is used by to detect normal and attacks data. In the least square support vector machinebased classification uses an enhanced SVM to avoid local minima This method detects all types of attacks with improved accuracy. The neuro-tree classifier provides effective classification when optimal features are provided It reduces the false alarm rate effectively, and in addition, the algorithm converges fast. The attributes Fi having maximum number of nonzero values are chosen by the agent, and the information gain ratio is computed using Equations 1, 2, and 3, where F is the feature set
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