MuDeLA: multi-level deep learning approach for intrusion detection systems

AbstractIn recent years, deep learning techniques have achieved significant results in several fields, like computer vision, speech recognition, bioinformatics, medical image analysis, and natural language processing. On the other hand, deep learning for intrusion detection has been widely used, particularly the implementation of convolutional neural networks (CNN), multilayer perceptron (MLP), and autoencoders (AE) to classify normal and abnormal. In this article, we propose a multi-level deep learning approach (MuDeLA) for intrusion detection systems (IDS). The MuDeLA is based on CNN and MLP to enhance the performance of detecting attacks in the IDS. The MuDeLA is evaluated by using various well-known benchmark datasets like KDDCup'99, NSL-KDD, and UNSW-NB15 in order to expand the comparison with different related work results. The outcomes show that the proposed MuDeLA achieves high efficiency for multiclass classification compared with the other methods, where the accuracy reaches 95.55% for KDDCup'99, 88.12% for NSL-KDD, and 90.52% for UNSW-NB15.Keywords: Intrusion detection systemmultilevel learning modeldeep learningconvolution neural networkmultilayer perceptron Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsWathiq Laftah Al-YaseenWathiq Laftah Al-Yaseen is currently a Lecturer in the Department of Computer Systems Techniques at Kerbala Technical Institute in Al-Furat Al-Awsat Technical University, Kerbala, Iraq. He received his Master of Computer Science from the University of Babylon, Iraq. He received his PhD of Computer Science from FTSM/UKM, Malaysia. His research interests include artificial intelligence, network security, machine learning, data mining and bioinformatics.Ali Kadhum IdreesAli Kadhum Idrees received his BSc and MSc in Computer Science from the University of Babylon, Iraq in 2000 and 2003 respectively. He received his PhD in Computer Science (wireless networks) in 2015 from the University of Franche-Comte (UFC), France. He is currently an Assistant Professor in Computer Science at the University of Babylon, Iraq. He has several research papers in wireless sensor networks (WSNs) and computer networks. His research interests include wireless networks, WSNs, SDN, IoT, distributed computing, data mining and optimisation in communication networks.