Performance enhancement of intrusion detection system using dimensionality reduction techniques and evaluation with different machine learning classifiers on optimal dataset

Abstract

Traffic classification is an automated process which categorizes newlinecomputer network traffic based on various parameters such as port number or newlineprotocol. Traffic classification is an essential tool for network and system newlinesecurity in complex environment. Intrusion detection is a monitoring newlinesystem that detects suspicious activities and generates alerts. Network newlineIntrusion Detection Systems (NIDS) play an important role to monitor newlineand analyze network traffic to protect a system from network-based threats. newlineThe Intrusion Detection Systems (IDS) are of different types - Active and newlinepassive IDS, Network Intrusion Detection Systems (NIDS), Host Intrusion newlineDetection Systems (HIDS), Knowledge-based (Signature-based) IDS and newlinebehavior-based (Anomaly-based) IDS. The Active IDS is also known as newlineIntrusion Detection and Prevention System and Passive IDS is configured to newlineonly monitor and analyze network traffic activity and alert an operator to newlinepotential vulnerabilities and attacks. newlineA Network-based Intrusion Detection System (NIDS) detects newlinemalicious traffic on a network. Host-based IDS runs on a host and monitors newlinesystem activities for signs of suspicious behavior. Signature-based detection is newlinetypically best used for identifying known threats. Anomaly-based intrusion newlinedetection systems can alert the suspicious behavior that is unknown. Network newlineTraffic datasets are captured from real time network using packet sniffer and newlineanalysis tool. The intrusion detection system developed based on flow and newlinepayload statistical features with clustering technique requires more number of newlineclusters for un-identified traffic network. Also it is difficult to map large newlinenumber of clusters to small number of real time applications. Though this newlinemethod is more effective, the design process is more complex. The research newlinerequires suitable feature selection algorithms and optimal dataset to enhance newlinethe accuracy. newline