SETA++: Real-Time Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

Kattadige, Chamara; Choi, Kwon Nung; Wijesinghe, Achintha; Nama, Arpit; Thilakarathna, Kanchana; Seneviratne, Suranga; Jourjon, Guillaume

doi:10.1109/tnsm.2021.3085097

Cited by 15 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An open-source Intrusion Detection System, Snort and Suricata [ 78 ], explains how to evaluate the drop rates and accuracy rates in a 100 Gbps network using their comparison and benchmarks [ 79 ]. This evaluation includes the usage of system resources, packet processing speed, packet drop ratio, and detection accuracy.…”

Section: Ddos Attack Detection In High-speed Networkmentioning

confidence: 99%

High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Ddos Attack Detection In High-speed Networkmentioning

confidence: 99%