Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

Abstract

Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment. Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period.

A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

“…Feature selection is considered one of the main parts of IDS because these systems have to deal with a large amount of data so a strong feature reduction technique is always encouraged to be applied with the network classification problem. Researchers [34,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] have used different feature selection techniques. The gain ratio, Pearson correlation, and ANOVA are few of the techniques that are widely used.…”

“…A feature which is repeating in any of the two subsets was used for the final subset. We combined the results of the multiple feature selection technique as it helps to find the most relevant and strong features and improves classification accuracy [38]. Figure 2 describes the complete feature selection model proposed and used in the paper.…”

Wk-fnn design for detection of anomalies in the computer network traffic

“…Feature selection has direct influence on the efficiency of the results and offers a way to reduce computation time, improve accuracy, and enable a better understanding of the classification models or the data. In the case of an anomaly detection, the labels assigned to the data instances are usually in the form of binary values [16]. Machine learning models can be very effective in learning normal or abnormal patterns from training data and in detection of the anomalies in the computer networks [17].…”

A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis

“…A RFID tracking attack attempts to disable tags, modify their contents, or imitate them. Various security solutions are proposed to overcome this attack, such as using a localized fault-detection algorithm to identify the faulty nodes in the WSN [23], using a decentralized intrusion-detection system model for the WSN [21], and introducing a derived intrusion-detection probability in both homogeneous and heterogeneous WSNs [24]. Physical threats to the RFID system are disabling tags, modifying their content, and imitating them [19].…”