IDS is the main concern of the security which is useful to prevent the attack at host and network level. In this propose work, classification of KDD intrusion dataset is proposed along with noise reduction, clustering and feature selection. DBSCAN algorithm has been applied to reduce noise present in KDD dataset. After noise removal genetic search approach is utilize to pick relevant feature. K-Means++ clustering method is utilized to cluster the dataset and resultant dataset is tested by SMO based classifier. A confirmation of concept prototype has been implemented to examine the performance of proposed approach using WEKA and MATLAB data mining tools. It is observed that proposed methods gives 96.922% accuracy. A comparative analysis performed between proposed methods and KMSVM (Simple K-mean with SVM classification) and it is observed that proposed method gives better results.

Everyone is now relying on the Internet for our innumerable kind of work; this has increased the opportunity for attackers to corrupt data and make vulnerable. Nowadays diverse kind of attacks is being launched in Cyber Space among which Cross-Site Scripting (Web Application Attack) is amongst top attacks of all time. Proposed work, suggest an outline for a system that can detect Cross-Site Scripting (known as XSS) attack using Intrusion Detection system (IDS). This work focuses on the detection of XSS attack using intrusion detection system. Here attack signature is utilized to detect XSS attack. To test the usefulness and effectiveness of proposed work a proof of concept prototype has been implemented using SNORT IDS. It is observed that proposed system correctly detected XSS attack.

Network intrusion detection system (NIDS) tracks network traffic for suspicious activity and policy violations. It generates alerts whenever such activity found. The objective is to detect and report anomalies. Further intrusion prevention system can take action such as blocking traffic from suspected IP addresses. Classification of network traffic as is a tedious task. Existing classifiers are suffered by generating many/false alerts. It is paramount important to select best classification approach among set of available approaches. KDD 99 is the benchmark dataset utilized to test the classification capabilities of classifiers. However, many classifiers generate similar results by measuring performance on various criteria. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a traditional multi-criteria decision making (MCDM) approach which is widely used to rank classifiers from number of options that are assessed on various criteria. In this work, KDD 99 dataset is applied as input to bayes net, naive bayes, NB updateable, random forest, oneR, zeroR, adaboostM1, decision stump, J48 and decision table classifiers. The performance of each classifier is measured using 10 different criteria’s such as accuracy, misclassification, RA error, RMS error, false positive rate, f- measure, precision, RRS error, mean absolute error and recall. In order to test the effectiveness of proposed approach weka utility is utilized for classification and classifier performance result are supplied to the TOPSIS. An application is designed to implement TOPSIS method using python. It is observed that J48 secured at the top position with performance score 0.5829.