Version 1
: Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (11:38:41 CET)
Version 2
: Received: 16 December 2020 / Approved: 17 December 2020 / Online: 17 December 2020 (12:14:00 CET)
Abu Al-Haija, Q.; Zein-Sabatto, S. An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Electronics2020, 9, 2152.
Abu Al-Haija, Q.; Zein-Sabatto, S. An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Electronics 2020, 9, 2152.
Abu Al-Haija, Q.; Zein-Sabatto, S. An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Electronics2020, 9, 2152.
Abu Al-Haija, Q.; Zein-Sabatto, S. An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Electronics 2020, 9, 2152.
Abstract
With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.
MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Qasem Abu Al-Haija
Commenter's Conflict of Interests: Author