- Title
- Partial undersampling of imbalanced data for cyber threats detection
- Creator
- Moniruzzaman, Md; Bagirov, Adil; Gondal, Iqbal
- Date
- 2020
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171731
- Identifier
- vital:14391
- Identifier
-
https://doi.org/10.1145/3373017.3373026
- Identifier
- ISBN:9781450376976 (ISBN)
- Abstract
- Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.; E1
- Publisher
- Association for Computing Machinery
- Relation
- 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Rights
- Copyright © 2020 Association for Computing Machinery
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- Clustering; Cyber threats; Imbalanced data.; Supervised learning
- Full Text
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