Presently, Network security is the most concerned subject matter because with the rapid use of internet technology and further dependence on network for keeping our data secure, it’s becoming impossible to protect from vulnerable attacks. Intrusion detection systems (IDS) are the key solution for detecting these attacks so that the network remains reliable. There are different classification approaches used to implement IDS in order to increase their efficiency in terms of detection rate. Support vector machine (SVM) is used for classification in IDS due to its good generalization ability and non linear classification using different kernel functions and performs well as compared to other classifiers. Different Kernels of SVM are used for different problems to enhance performance rate. In this paper, we provide a review of the SVM and its kernel approaches in IDS for future research and implementation towards the development of optimal approach in intrusion detection system with maximum detection rate and minimized false alarms.

A Review of Classification Approaches Using Support Vector Machine in Intrusion Detection

--------------------------------------------------------------- Mobile Ad hoc Networks (MANETS) are transient networks ofmobile nodes, connected through wireless links, without any fixed infrastructure or central management. Due to the self-configuring nature of these networks, the topology is highly dynamic. This makes the Ad Hoc Routing Protocols in MANETS highly vulnerable to serious security issues. In this paper, we survey the common security threats and attacks and summarize the solutions suggested in the survey to mitigate these security vulnerabilities. Keywords- Ad hoc Routing Protocols, AODV, DSDV, DSR, MANETs, Security attacks, Performance Analysis ------------------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: January 26, 2012 Date of Acceptance: February 20, 2012 -------------------------------------------------------------------------------------------------------------------------------------------------

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A Survey on Security Issues in Ad Hoc Routing Protocols and their Mitigation Techniques

The massive usage of network-based communication technology to satisfy the user's requirements opens the way for enormous network attacks to be launched. An intrusion detection system (IDS) is the most effective solution for the detection of those attacks. In this paper, we discuss the IDS types; especially machine learning-based IDS. A large research area in IDS technologies is concerned with identifying known and unknown attacks. This paper provides the most common machine learning IDS systems. Also, we discuss the IDS requirements for perfect detection. Furthermore, a comparison between them in terms of detection accuracy and system performance is presented.

Network Intrusion Detection, Literature Review and Some Techniques Comparision

In order to improve the classification accuracy and reduce the detection time, the optimization of feature extraction and SVM training model is combined together. In the procedure of feature extraction using CEGA with adaptive crossover and mutation, fitness of the individual is evaluated by the correct classification rate and conditional entropy. The optimization of SVM training model is processed at the same time with the feature extraction in order to find the best combination of optimal feature subset with the SVM training model. Results of the experiment using KDD CUP99 data sets demonstrate that applying CEGA-SVM can be an effective way for feature extraction and intrusion detection.

Yuxin Wei

Papers

Intrusion detection technology based on CEGA-SVM