Artificial Intelligence for Cybersecurity: Offensive Tactics, Mitigation Techniques and Future Directions

Erwin Adi; Zubair Baig; Sherali Zeadally

doi:10.5604/01.3001.0016.0800

Artificial Intelligence for Cybersecurity: Offensive Tactics, Mitigation Techniques and Future Directions

Erwin Adi
, Zubair Baig
, Sherali Zeadally

School of Information Science

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Cybersecurity has benefitted from Artificial Intelligence (AI) technologies for attack detection. However, recent advances in AI techniques, in tandem with their misuse, have outpaced parallel advancements in cyberattack classification methods that have been achieved through academic and industry-led efforts. We describe the shift in the evolution of AI techniques, and we show how recent AI approaches are effective in helping an adversary attain his/her objectives appertaining to cyberattacks. We also discuss how the current architecture of computer communications enables the development of AI-based adversarial threats against heterogeneous computing platforms and infrastructures.

Original language	English
Journal	Applied Cybersecurity and Internet Governance
Volume	1
Issue number	1
DOIs	https://doi.org/10.5604/01.3001.0016.0800
State	Published - 2022

Bibliographical note

Publisher Copyright:
© Erwin Adi, Zubair Baig, Sherali Zeadally, Publisher NASK. Publishing House by Index Copernicus Sp. z. o.

Funding

We thank the anonymous reviewers for their valuable comments of assistance to us in improving the content, organisation, and presentation of this paper. Sherali Zeadally was supported by a Fulbright U.S. Scholar Grant ?ward administered by the U.S. Department of State’s Bureau of Educational and Cultural ?ffairs, and through its cooperating agency the Institute of International Education (“IIE”). We thank the anonymous reviewers for their valuable comments of assistance to us in improving the content, organisation, and presentation of this paper. Sherali Zeadally was supported by a Fulbright U.S. Scholar Grant Award administered by the U.S. Department of State’s Bureau of Educational and Cultural Affairs, and through its cooperating agency the Institute of International Education (“IIE”).

Funders
Fulbright U.S. Scholar
U.S. Department of State Bureau of Educational and Cultural Affairs
Institute of International Education

Keywords

adversarial AI
cyber infrastructures
data analysis
supply chain compromise

ASJC Scopus subject areas

Computer Science (miscellaneous)
Psychology (miscellaneous)
Human-Computer Interaction
Political Science and International Relations

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10.5604/01.3001.0016.0800

Cite this

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abstract = "Cybersecurity has benefitted from Artificial Intelligence (AI) technologies for attack detection. However, recent advances in AI techniques, in tandem with their misuse, have outpaced parallel advancements in cyberattack classification methods that have been achieved through academic and industry-led efforts. We describe the shift in the evolution of AI techniques, and we show how recent AI approaches are effective in helping an adversary attain his/her objectives appertaining to cyberattacks. We also discuss how the current architecture of computer communications enables the development of AI-based adversarial threats against heterogeneous computing platforms and infrastructures.",

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AB - Cybersecurity has benefitted from Artificial Intelligence (AI) technologies for attack detection. However, recent advances in AI techniques, in tandem with their misuse, have outpaced parallel advancements in cyberattack classification methods that have been achieved through academic and industry-led efforts. We describe the shift in the evolution of AI techniques, and we show how recent AI approaches are effective in helping an adversary attain his/her objectives appertaining to cyberattacks. We also discuss how the current architecture of computer communications enables the development of AI-based adversarial threats against heterogeneous computing platforms and infrastructures.

KW - adversarial AI

KW - cyber infrastructures

KW - data analysis

KW - supply chain compromise

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ER -

Artificial Intelligence for Cybersecurity: Offensive Tactics, Mitigation Techniques and Future Directions

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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