- Title
- Detection of anomalies and explanation in cybersecurity
- Creator
- Samariya, Durgesh; Ma, Jiangang; Aryal, Sunil; Zhao, Xiaohui
- Date
- 2024
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199887
- Identifier
- vital:19289
- Identifier
-
https://doi.org/10.1007/978-981-99-8178-6_32
- Identifier
- ISBN:1865-0929 (ISSN); 9789819981779 (ISBN)
- Abstract
- Histogram-based anomaly detectors have gained significant attention and application in the field of intrusion detection because of their high efficiency in identifying anomalous patterns. However, they fail to explain why a given data point is flagged as an anomaly. Outlying Aspect Mining (OAM) aims to detect aspects (a.k.a subspaces) where a given anomaly significantly differs from others. In this paper, we have proposed a simple but effective and efficient histogram-based solution - HMass. In addition to detecting anomalies, HMass provides explanations on why the points are anomalous. The effectiveness and efficiency of HMass are evaluated using comparative analysis on seven cyber security datasets, covering the tasks of anomaly detection and outlying aspect mining. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, 20-23 November 2023, Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20-23, 2023, Proceedings, Part XIII Vol. 1967 CCIS, p. 414-426
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2024, The Author(s)
- Subject
- Anomaly detection; Anomaly explanation; Cyber security; Outlier explanation; Outlying aspect mining
- Reviewed
- Funder
- Federation University Research Priority Area (RPA) scholarship
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