In the last three decades computer networks have grown in size and complexity drastically. This tremendous growth has posed challenging issues in network and information security, and detection of security threats, commonly referred to as intrusion, has become a very important and critical issue in network, data and information security. The security attacks can cause severe disruption to data and networks. Therefore, Intrusion Detection System (IDS) becomes an important part of every computer or network system. An IDS can monitor computer or network traffic and identify malicious activities that compromise the integrity, confidentiality, and availability of information resources and alerts the system or network administrator against malicious attacks. Since, an IDS needs to examine very large data with high dimension even for small network. Due to this, IDS has to meet the challenges of low detection rate and large computation.
Therefore, Feature selection is a very important issue and plays a key role in intrusion detection in order to achieve maximal performance. It is one of the important and frequently used techniques in data preprocessing for selecting a subset of relevant features to build robust IDS. Feature selection is the selection of that minimal cardinality feature subset of original feature set that retains the high detection accuracy as the original feature set. The efficient feature subset can improve the training and testing time that helps to build lightweight IDS guaranteeing high detection rates and makes IDS suitable for real time and on-line detection of attacks.
This survey paper categorizes the feature selection algorithms that have been developed for IDS building, critically evaluates their usefulness, and recommends ways of enhancing the quality of feature selection algorithms.
The paper is organized into the following sections. Intrusion Detection Systems is reviewed in Section 2. Section 3 gives the details of the Datasets and Performance Evaluation used in this survey. In Section 4, different methodologies of feature selection in IDSs are discussed. Related research in the literature for feature selection methods together with their performance is addressed in Section 5. Section 6 summaries the different results reported in the literature in tabular form. Section 7 concludes and discusses future research.