Denial-of-Service (DoS) Threat Detection Using Supervised Machine Learning Algorithms on CICIDS2018 Dataset

Saleh, Abu Jafar Mohammad; Adnan, Nasim

doi:10.1007/978-981-19-2445-3_36

Lecture Notes in Networks and Systems

2022

DOI: 10.1007/978-981-19-2445-3_36

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Denial-of-Service (DoS) Threat Detection Using Supervised Machine Learning Algorithms on CICIDS2018 Dataset

Abu Jafar Mohammad Saleh¹,

Nasim Adnan²

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2023

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High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

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High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

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Denial-of-Service (DoS) Threat Detection Using Supervised Machine Learning Algorithms on CICIDS2018 Dataset

Cited by 1 publication

References 21 publications

High-Speed Network DDoS Attack Detection: A Survey

High-Speed Network DDoS Attack Detection: A Survey

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