A deep learning method with wrapper based feature extraction for wireless intrusion detection system. Issue 92 (May 2020)

• Record Type: Journal Article
• Authors: Kasongo, Sydney Mambwe; Sun, Yanxia
• Is Part Of: Computers & security. Issue 92(2020)
• Publication Date: 2020-05
• Subjects: Machine learning -- Deep learning -- Intrusion detection -- Wireless networks -- Feature extraction; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2020.101752
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: In the past decade, wired and wireless computer networks have substantially evolved because of the rapid development of technologies such as the Internet of Things (IoT), wireless handled devices, vehicular networks, 4G and 5G, cyber-physical systems, etc. These technologies exchange large amount of data, and as a result, they are prone to several malicious actions, attacks and security threats that can compromise the availability and integrity of information or services. Therefore, the security and protection of the various communication infrastructures using an intrusion detection system (IDS) is of critical importance. In this research, we propose a Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU). The extraction method of the WFEU uses the Extra Trees algorithm in order to generate a reduced optimal feature vector. The effectiveness and efficiency of the WFEU-FFDNN is studied based on the UNSW-NB15 and the AWID intrusion detection datasets. Furthermore, the WFEU-FFDNN is compared to standard machine learning (ML) algorithms that include Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT) and k-Nearest Neighbor (kNN). The experimental studies include binary and multiclass types of attacks. The results suggested that the proposed WFEU-FFDNN has greater detection accuracy than other approaches. In the instance of the UNSW-NB15, the WFEU generated an optimal feature vector consisting of 22 attributes. Using this input vector; our approach achieved overall accuracies of 87.10% and 77.16% for the binary and multiclass classification schemes, respectively. In the instance of the AWID, a reduced input vector of 26 attributes was generated by the WFEU, and the experiments demonstrated that our method obtained overall accuracies of 99.66% and 99.77% for the binary and the multiclass classification configurations, respectively.