AI and machine learning: A mixed blessing for cybersecurity

Author First name, Last name, Institution

Faouzi Kamoun
Farkhund Iqbal
Mohamed Amir Esseghir
Thar Baker

Document Type

Conference Proceeding

Source of Publication

2020 International Symposium on Networks, Computers and Communications, ISNCC 2020

Publication Date

10-20-2020

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.

DOI Link

10.1109/isncc49221.2020.9297323

ISBN

9780000000000

Publisher

Institute of Electrical and Electronics Engineers Inc.

Disciplines

Computer Sciences

Keywords

Computer networks, Machine learning, Attack vector, Cyber security, Cyber-attacks, Machine learning software, System faults, Wide spectrum, Security of data

Scopus ID

85099583215

Recommended Citation

Kamoun, Faouzi; Iqbal, Farkhund; Esseghir, Mohamed Amir; and Baker, Thar, "AI and machine learning: A mixed blessing for cybersecurity" (2020). All Works. 383.
https://zuscholars.zu.ac.ae/works/383

Indexed in Scopus

yes

Open Access

no