Intrusion Detection Using Feature Selection and Machine Learning Algorithm with Misuse Detection

Abstract

In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical issues. An intruder may enter any network or system or server by intruding malicious packets into the system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose of intrusion is referred to as attack. With the expanding networking technology, millions of servers communicate with each other and this expansion is always in progress every day. Due to this fact, more and more intruders get attention; and so to overcome this need of smart intrusion detection model is a primary requirement. By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is done, then by using selected features and machine learning approach and analyzing the basic features of networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm containing the rules for the network features. A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion detection. Based on prior features, intrusions on the system can be detected without any previous learning. This model contains the advantage of feature selection and machine learning techniques with misuse detection.

Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms

“…In the research of intrusion detection, redundant features can degrade detection performance, so more and more researchers focus on feature selection [2,16,18,20]. The process of feature selection in this paper is similar to the data sampling.…”

“…IDSs are generally classified into two categories: signature-based and anomaly-based detection systems [1]. Signature-based intrusion detection systems [2,3], such as Snort intrusion detection systems [3], are designed to detect intrusion by building anomaly behavior character libraries and matching network data. These IDSs have high detection rate, but they are difficult to identify new attacks in the network.…”

A Comparative Study of Data Mining Algorithms for High Detection Rate in Intrusion Detection System

“…For attributes assessment, DT is produced originally based on feature values achieved through training data. The classification of data examples can be made based on attributes influence by trained test data [12]. Random Forest is a supervised learning classifier which based on a group of three analysts.…”

Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection

“…ere are mainly two types of intrusion detection techniques based on the approach followed for detecting network intrusion: signature and anomaly-based intrusion detection model [6,7]. On the basis of a computer system, anomaly-based intrusion detection approach identifies abnormal behavior of the network traffic by creating baseline on the normal behavior of network traffics.…”