Adversarial and Offensive AI in Cyber Security

As Artificial Intelligence (AI) continues to advance swiftly and integrates into various domains, cybersecurity becomes increasingly crucial for navigating both the benefits and pitfalls of AI technologies. This paper explores how adversarial and offensive artificial intelligence (AI) techniques affect cybersecurity defense methods. The research offers important insights from analyzing recent cyber-attacks that use adversarial AI, showing a marked rise in the complexity of these threats when compared to traditional security frameworks. Key results indicate that older cybersecurity defenses are becoming less effective against attacks that are boosted by AI, making it essential to create flexible strategies that use offensive AI techniques to forecast and stop possible breaches. By understanding the possible risks posed by AI, organizations can strengthen their defenses against new cyber threats. Additionally, this study emphasizes the need for a major change in how organizations approach cybersecurity, suggesting a proactive approach that recognizes the dual-use nature of AI tools. The wider implications of this research indicate that applying advanced AI methods in cybersecurity not only improves system resilience but also fosters a culture of ongoing improvement and awareness, ultimately protecting organization essential infrastructure from changing cyber threats.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-10
DOI
https://doi.org/10.5281/zenodo.14866277
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Adversarial and Offensive AI in Cyber Security

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Adversarial and Offensive AI in Cyber Security

article en

Abstract

As Artificial Intelligence (AI) continues to advance swiftly and integrates into various domains, cybersecurity becomes increasingly crucial for navigating both the benefits and pitfalls of AI technologies. This paper explores how adversarial and offensive artificial intelligence (AI) techniques affect cybersecurity defense methods. The research offers important insights from analyzing recent cyber-attacks that use adversarial AI, showing a marked rise in the complexity of these threats when compared to traditional security frameworks. Key results indicate that older cybersecurity defenses are becoming less effective against attacks that are boosted by AI, making it essential to create flexible strategies that use offensive AI techniques to forecast and stop possible breaches. By understanding the possible risks posed by AI, organizations can strengthen their defenses against new cyber threats. Additionally, this study emphasizes the need for a major change in how organizations approach cybersecurity, suggesting a proactive approach that recognizes the dual-use nature of AI tools. The wider implications of this research indicate that applying advanced AI methods in cybersecurity not only improves system resilience but also fosters a culture of ongoing improvement and awareness, ultimately protecting organization essential infrastructure from changing cyber threats.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 68%
Adversarial Robustness in Machine Learning
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Adversarial and Offensive AI in Cyber Security · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS