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

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.

DDoS attacks in cloud environment

“…DDoS flood packets were sent by taking advantage of a vulnerability in the virtual machine. DDoS assaults and cryptocurrency mining were shown in a botnet built by Bishop Fox at the black hat conference [14] and [20]. UDP/ICMP and HTTPS DDoS assaults were used to infiltrate banking websites [15].…”

“…At the gateway router, the attack packets are discarded with minimal computational overhead. An attack analyzer is part of the networkbased detection [20], which includes the network controller and a profile server. The analyzer determines the most effective defenses, and the network controller implements them.…”

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

“…They developed a state-of-the-art method known as correlated-set thresh-holding on the voltage increase (CST-GR), which only employs the attributes mandatory for separate hacks. [33] Created an IoT-based intrusion detection and classification platform using deep learning as well as a neural network model (IoT-IDCS-CNN). Feature extraction, segmentation techniques, and system identification compose the methods.…”