Intrusion detection system based on GA-fuzzy classifier for detecting malicious attacks

摘要

Usage of computer resources, being a very important part in day to day life, it is to be noticed that the security threats have also increased. Hence, Intrusion Detection System (IDS) is used for detection and prevention of computer resources from security threats generated by malicious attackers. Existing techniques like encryption mechanism, authentication mechanism, and access control do not support for analyzing large volume of data and it is efficient only in the case of limited number of attacks. Attackers attack the computer resources based on the weakness of the security level in the Information system and they can violate the rules and regulation of computer system (Confidentiality, Integrity, and Availability) easily. Handling threats on computer resources still remains a challenging issue. Distributed Denial of Service attacks (DDoS) is an important attack that sends more than one number of requests to the destination server from multiple compromised systems that makes the Information system unable to process the request thereby resulting in non-response to the attacker as well as normal end user, which results in large number of false alarms and less detection accuracy rates. We propose a new model called hybrid-based intrusion detection system (GA-Fuzzy) for handling large volume NSL-KDD Dataset for detecting attacks effectively and for reducing misclassification alarm rate. Here, Genetic algorithm (GA) is used for creating new pattern (new features, records) for training the Fuzzy classifier effectively. We use Principle Component Analysis (PCA) as a feature selection method that eliminates irrelevant and redundant data from the NSL-KDD dataset that improves the efficiency and to attain 99.96% detection accuracy and 0.04% false alarm rate.