ASU Electronic Theses and Dissertations
- 1 English
Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms. In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to …
- Almukaynizi, Mohammed, Shakarian, Paulo, Huang, Dijiang, et al.
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