Soft Computing and Artificial Intelligence Techniques for Intrusion Detection System

The rapid development of computer networks and mostly of the Internet has created many stability and security problems such as intrusions on computer and network systems. Further the dependency of companies and government agencies is increasing on their computer networks and the significance of protecting these systems from attacks is serious because a single intrusion can cause a heavy loss or the consistency of network becomes insecure. During recent years number of intrusions has dramatically increased. Therefore it is very important to prevent such intrusions. The hindrance of such intrusions is entirely dependent on their detection that is a key part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Hence accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Keywords: IDS, Soft Computing, ANN, Genetic Algorith

Improved Performance of Network Attack Detection using Combination Data Mining Techniques

Network Attack detection is very important mechanism for detecting attack in computer networks. Data mining techniques play very important role in detecting intrusions in computer networks. Intrusions can damage to the data and compromise integrity and confidentiality and availability of the data. Intrusions are the activities that violate the security policy of system. Intrusion Detection is the process used to identify network attack. Network security is to be considered as a major issue in recent years, since the computer network keeps on expanding every day. A Network Attack Detection System (NADS) is a system for detecting intrusions and reporting to the authority or to the network administration. Data mining techniques have been applied in many fields like Network Management, Education, Science, Business, Manufacturing, Process control, and Fraud Detection. Data mining algorithms like J48, Randam Forest ,Random Tree, Hoefding Tree and Rep Tree are used to build intrusion detection models using KDD CUP 1999. The performance of network attack detection model is evaluated using KDD CUP 1999 test dataset using series of experiments and measured using correct classi?cation and detection of attack. The combination of data mining algorithm will increase performance of network attack detection i.e false positive and false negative, novel or unknown attacks