Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

Version 1 : Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (11:38:41 CET)

How to cite: Abu Al-Haija, Q.; McCurry, C.; Zein-Sabatto, S. An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Preprints 2020, 2020110508 (doi: 10.20944/preprints202011.0508.v1). Abu Al-Haija, Q.; McCurry, C.; Zein-Sabatto, S. An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Preprints 2020, 2020110508 (doi: 10.20944/preprints202011.0508.v1).

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.

Subject Areas

Deep Learning; Convolutional Neural Network; IoT Networks; Cyber-attack detection; Cyber-attack Classification

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