Abstract
While the usage of Artificial Intelligence and Machine Learning Software (AI/MLS) in defensive cybersecurity has received considerable attention, there remains a noticeable research gap on their offensive use. This paper reviews the defensive usage of AI/MLS in cybersecurity and then presents a survey of its offensive use. Inspired by the System-Fault-Risk (SFR) framework, we categorize AI/MLS-powered cyberattacks by their actions into seven categories. We cover a wide spectrum of attack vectors, discuss their practical implications and provide some recommendations for future research.
| Original language | English |
|---|---|
| Title of host publication | 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728156286 |
| DOIs | |
| Publication status | Published - 20 Oct 2020 |
| Event | 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020 - Montreal, Canada Duration: 20 Oct 2020 → 22 Oct 2020 |
Publication series
| Name | 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020 |
|---|
Conference
| Conference | 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 20/10/20 → 22/10/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Adversarial techniques
- AI
- Cybersecurity
- Deep learning
- Machine learning
- Neural networks
- Security