Deep Learning Techniques for Cyber Security Intrusion Detection : A Detailed Analysis

Abstract

In this study, we present a detailed analysis of deep learning techniques for intrusion detection. Specifically, we analyze seven deep learning models, including, deep neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machine, deep belief networks, deep Boltzmann machines, and deep autoencoders. For each deep learning model, we study the performance of the model in binary classification and multiclass classification. We use the CSE-CIC-IDS 2018 dataset and TensorFlow system as the benchmark dataset and software library in intrusion detection experiments. In addition, we use the most important performance indicators, namely, accuracy, detection rate, and false alarm rate for evaluating the efficiency of several methods.

Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

“…In order to test the efficiency of such mechanisms, reliable datasets are needed that (i) contain both benign and several attacks, (ii) meet real world criteria, and (iii) are publicly available [6]. This paper extends our work in [7].…”

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

“…First, to accept nonlinear transformation and create a statistical model as output and the second, to improve the model with derivatives (mathematical methods). In between a huge combination of hidden layers establish network connection [50]. The most popular DL techniques implemented for IDS are discussed here.…”

“…Convolutional Neural Network (CNN): It is another most suitable for network intrusion detection to analyse traffic and classify good and bad node; it provides end-to-end security. High computational cost and limited to resource-constrained devices are the major falloffs of this technique [50]. Considering these many qualities of DL techniques, research community have provided various detection and prevention models to mitigate the effects of vulnerabilities in IIoTs and a summarization is shown in Table 6.…”

RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

“…Both DeepCoin and DeliveryCoin frameworks demonstrated the efficiency of the deep learning techniques in cyber security intrusion detection. For more detail about the deep learning techniques in cyber security intrusion detection, we refer the reader to our recent studies in [26,27].…”