A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance
This addresses cybersecurity challenges for network security systems, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The study tackled DDoS attack detection under class imbalance and limited labeled data by evaluating 13 semi-supervised learning algorithms, providing insights for designing robust intrusion detection systems.
One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex process due to the inherent class imbalance and lack of sufficient labeled samples of real-world datasets. This research investigates the use of Semi-Supervised Learning (SSL) techniques to improve DDoS attack detection when data is imbalanced and partially labeled. In this process, 13 state-of-the-art SSL algorithms are evaluated for detecting DDoS attacks in several scenarios. We evaluate their practical efficacy and shortcomings, including the extent to which they work in extreme environments. The results will offer insight into designing intelligent Intrusion Detection Systems (IDSs) that are robust against class imbalance and handle partially labeled data.