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, the Internet of Things (IoT) has earned 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 that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (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.

High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

“…The classification process is an intelligent task that predicts the class label of a given data record by utilizing machine learning algorithms [24]. Machine learning algorithms used to build predictive modeling to approximate the mapping for the target output based on a number of features are called supervised machine learning models [25].…”

IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

“…Various papers are available regarding the applicability of machine learning and Artificial Neural Network algorithms for intrusion detection in IoT ecosystems [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], but research in this direction is still in its infancy and requires further study and improvements. Next, we briefly present and discuss the most relevant of these papers for our work.…”

“…A solution for classifying IoT attacks using neural networks is proposed in [16]. The authors developed, tested, and validated a system that uses Convolutional Neural Networks to detect and classify IoT attacks inside the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset.…”

“…After eliminating the features considered irrelevant for our experiment, the number of inputs for our autoencoder remained 16. Therefore, the number of outputs was also 16.…”

Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

“…Furthermore, an unsupervised ensemble learning method in [16] and a deep autoencoder model in [17] have been developed to detect false data injection attacks in PMUs, whereas PMUs' data are difficult to label due to their fast data streaming. Although deep learning methods exhibit impressive results in detecting FDIAs [18,19], they require large training data sets, high computational costs, and specialized equipment.…”