A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911423255281664 |
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| author | Hallaji, Ehsan Shanmugam, Vaishnavi Razavi-Far, Roozbeh Saif, Mehrdad |
| author_facet | Hallaji, Ehsan Shanmugam, Vaishnavi Razavi-Far, Roozbeh Saif, Mehrdad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance Hallaji, Ehsan Shanmugam, Vaishnavi Razavi-Far, Roozbeh Saif, Mehrdad Cryptography and Security Artificial Intelligence Machine Learning 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. |
| title | A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.22949 |