Multi-source cyber-attacks detection using machine learning
- Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
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- Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
The case for a consistent cyberscam classification framework (CCCF)
- Authors: Stabek, Amber , Brown, Simon , Watters, Paul
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at UIC-ATC 2009 - Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with the UIC'09 and ATC'09 Conferences, Brisbane : 7th-9th July 2009 p. 525-530
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- Description: Cyberscam classification schemes developed by international statistical reporting bodies, including the Bureau of Statistics (Australia), the Internet Crime Complaint Center (US), and the Environics Research Group (Canada), are diverse and largely incompatible. This makes comparisons of cyberscam incidence across jurisdictions very difficult. This paper argues that the critical first step towards the development of an inter-jurisdictional and global approach to identify and intercept cyberscams - and prosecute scammers - is a uniform classification system. © 2009 IEEE.