Scholarly works in the Department of Computer Science

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Genetic Algorithm Rule-Based Intrusion Detection System (GAIDS)
(Journal of Emerging Trends in Computing and Information Sciences, 2012-08) Ojugo, Arnold Adimabua; Eboka, A.O.; Okonta, O.E.; Yoro, R.E; Aghware, F.O.
This study examines the detection of attacks or network intrusion by users referred to as hackers (whose aim is to gain illegal entry as well as access to a network system and resources. Network and data security has become a pertinent issue with the advent of the Internet; though the Internet comes with a lot of merits on its own. Traditional used methods for data security includes the use of passwords, cryptography to mention few. The approach considered here is Intrusion Detection System, which is a software, driver or device used to prevent an unauthorized or illegal access to data in a networked system. Most of the existing IDS are implemented via rule-based systems where new attacks are not detectable. This study thus, presents a genetic algorithm based approach (with its driver implementation), which employs a set of classification rule derived from network audit data and the support-confidence framework, utilized as fitness function to judge the quality of each rule. The software implementation is aimed at improving system security in networked settings allowing for confidentiality, integrity and availability of system resources.