Review: Soft Computing Techniques (Data-Mining) On Intrusion Detection

With the tremendous growth of various web applications and network based services, network security has become an alarming issue in the vicinity of IT engineers. As the numerous amazing services come to the clients, so does the extensive growth of hackers on the backend. Intrusion poses a serious security risk in the networking environment. Too often, intrusion arises havoc in LANs and heavy loss of time and cost of repairing them. It is said that “prevention is better than cure”, so intrusion prevention systems (IPS) and the intrusion detection system (IDS) are used. In addition to the well established intrusion prevention schemes like encryption, client authorization and authentication, IDS can be viewed as a safety belt or fence for network framework. As, the use of interconnected networks have become common, so to have world-wide reports of vulnerabilities and intrusive attacks on systems have increased. CERT noted that between 2000 and 2006 over 26,000 distinct vulnerabilities were reported. An intrusion, which is the set of actions that compromise the integrity, confidentiality, or availability of any resource, and generally exploits one or more bottlenecks of the network. In this paper, we describe various data mining approaches applied on IDS that can be used to handle various network attacks and their comparative analysis.