Handling Web and Database Requests Using Fuzzy Rules for Anomaly Intrusion Detection

Abstract

Problem statement: It is necessary to propose suitable detection and prevention mechanisms to provide security for the information contents used by the web application. Many prevention mechanisms which are currently available are not able to classify anomalous, random and normal request. This leads to the problem of false positives which is classifying a normal request as anomalous and denying access to information. Approach: In this study, we propose an anomaly detection system which will act as a Web based anomaly detector called intelligent SQL Anomaly detector and it uses decision tree algorithm and a feedback mechanism for effective classification. Results: This newly proposed and implemented technique has higher probability for reducing false positives which are the drawbacks in the earlier systems. Hence, our system proves that it detects all anomalies and shows better results when compared with the existing system. Conclusion: A refreshing technique to improve the detection rate of web-based intrusion detection systems by serially framing a web request anomaly detector using fuzzy rules has been proposed and implemented and this system proves to be more efficient then the existing earlier system when compared with the obtained results.