| Title | Vulnerability Assessment for Machine Learning Based Network Anomaly Detection System |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ogawa, Yuji, Kimura, Tomotaka, Cheng, Jun |
| Conference Name | 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) |
| Keywords | anomaly detection, compositionality, feature extraction, Human Behavior, machine learning, machine learning algorithms, Metrics, Neural networks, pubcrawl, Resiliency, security, Training data, vulnerability detection |
| Abstract | In this paper, we assess the vulnerability of network anomaly detection systems that use machine learning methods. Although the performance of these network anomaly detection systems is high in comparison to that of existing methods without machine learning methods, the use of machine learning methods for detecting vulnerabilities is a growing concern among researchers of image processing. If the vulnerabilities of machine learning used in the network anomaly detection method are exploited by attackers, large security threats are likely to emerge in the near future. Therefore, in this paper we clarify how vulnerability detection of machine learning network anomaly detection methods affects their performance. |
| DOI | 10.1109/ICCE-Taiwan49838.2020.9258068 |
| Citation Key | ogawa_vulnerability_2020 |
Vulnerability Assessment for Machine Learning Based Network Anomaly Detection System