A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks

Abstract

(Distributed) Denial of Service (DoS/DDoS) attacks are performed to bring down a target by flooding it withuseless traffic. Because the DoS/DDoS attackers often change their styles and attack patterns, the nature andcharacteristics of these attacks need to be examined cautiously. Developing mechanisms to detect this menaceis a challenging task. Recently, deep learning has played a major role in the growth of intrusion detection solutions. In recent years, significant attempts have been made to construct deep learning models for counteringDoS/DDoS threats. In this review, we provide a taxonomy of DoS/DDoS attacks and deep learning-based DoS/DDoS detection approaches. Then, the article focuses on the recent (from 2016 onwards) defensive methodsagainst DoS/DDoS attacks that exploit the advantages of deep learning techniques and discusses the key features of each of them. As datasets are imperative for deep learning techniques, we also review the traditional and contemporary datasets that contain traces of DoS/DDoS attacks. The findings from the review articles are as well summarized and they urge that more effort be made to strengthen the existing state-of-the-art approaches to coping with the dynamic behavior of the attackers. The imbalances in the surveyed articles are also highlighted. Finally, we outline a few key research directions that will need additional focus in the near future to ensure good security against DoS/DDoS attacks using deep learning approaches.

Hybrid Strategy Improved Sparrow Search Algorithm in the Field of Intrusion Detection

“…With the continuous development of deep learning technology, it is also widely used in intrusion detection [20]. Different from traditional machine learning algorithms, deep learning technology can automatically learn data features without human intervention, which brings a new idea for processing multi feature intrusion data [21].…”

“…This is the author's version which has not been fully edited and content may change prior to final publication. F1 score, which is the harmonic average of recall rate and accuracy rate, it s a measure of a test's accuracy, the calculation process is shown in (20).…”

Light-Weight Slow-Rate Attack Detection Framework for Resource-Constrained Industrial Cyber-Physical Systems

“…Reviews of LDoS attacks exist [43,1,44,3,45], but none relates to resource-constrained ICPS. Similarly, some surveys review security concerns of the cyber-physical systems using ML/DL [46][47][48][49][50][51]; however, they do not cover LDoS or their detection.…”

Event-Based Pinning Synchronization Control for Discrete-Time Delayed Complex Cyber-Physical Networks Under All-Around Attacks

“…Due to the information interaction process between the nodes of complex cyber-physical networks through cyber space has become more complicated, thus complex cyber-physical networks are confronted with a larger risk of communications network-induced issues like network delay [29] and cyber attacks [28]. As an emerging field, security issues due to cyber attacks in the complex networks has attracted extensive attention and achieved a series of meaningful research results [10,11,12,19,34]. In general, there have been three cyber attacks that frequently arise in addressing the problem of secure control, i.e., false data injection (FDI) attacks [10,12,34], denial of service (DoS) attacks [19] and replay attacks [11].…”

“…As an emerging field, security issues due to cyber attacks in the complex networks has attracted extensive attention and achieved a series of meaningful research results [10,11,12,19,34]. In general, there have been three cyber attacks that frequently arise in addressing the problem of secure control, i.e., false data injection (FDI) attacks [10,12,34], denial of service (DoS) attacks [19] and replay attacks [11]. From the perspective of security level, FDI attacks are the most dangerous attack, because the attackers can inject malicious data to worsen or destabilize the performance of the target system.…”