A novel statistical analysis and autoencoder driven intelligent intrusion detection approach

Ieracitano, Cosimo; Adeel, Ahsan; Morabito, Francesco Carlo; Hussain, Amir

Abstract

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dangerous malware attacks that make intrusion recognition a very difficult task. In this context, traditional analytical tools are facing severe challenges to detect and mitigate these threats. In this work, we introduce a novel statistical analysis and autoencoder (AE) driven intelligent intrusion detection system (IDS). Specifically, the proposed IDS combines data analytics and statistical techniques with recent advances in machine learning theory to extract more optimized, strongly correlated features. The proposed IDS is evaluated using the benchmark NSL-KDD database. Comparative experimental results show that the designed statistical analysis and AE based IDS achieves better classification performance compared to conventional deep and shallow machine learning and other recently proposed state-of-the-art techniques.

Citation

Ieracitano, C., Adeel, A., Morabito, F. C., & Hussain, A. (2020). A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing, 387, 51-62. https://doi.org/10.1016/j.neucom.2019.11.016

• Journal Article Type: Article
• Acceptance Date: Nov 7, 2019
• Online Publication Date: Nov 13, 2019
• Publication Date: 2020-04
• Deposit Date: Apr 2, 2020
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 387
• Pages: 51-62
• DOI: https://doi.org/10.1016/j.neucom.2019.11.016
• Keywords: Cognitive Neuroscience; Artificial Intelligence; Computer Science Applications
• Public URL: http://researchrepository.napier.ac.uk/Output/2650382