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September 10, 2025World Journal of Advanced Research and Reviews

Robust detection and mitigation strategies against adversarial attacks on AI systems for enhanced cybersecurity resilience

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Authors

SOShadrack Onyango Oriaro

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Overview

Research examines adversarial attacks' impact on AI systems, suggesting robust defenses and threat detection methods.

Key Points

  • Robust defenses significantly improve AI systems' reliability against adversarial attacks and enhance cybersecurity.
  • Critical methods explored include adversarial training and data forensic techniques for identifying threats.
  • Baseline datasets and threat models are essential for effective testing and development of security measures.
  • AI professionals must address ongoing challenges in AI safety to handle evolving adversarial threats.

Cite This Study

Shadrack Onyango Oriaro (2025) studied this question.

synapsesocial.com/papers/68c1925e9b7b07f3a0617064https://doi.org/10.30574/wjarr.2025.27.3.2560
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Adversarial AI the New Frontier in Cybersecurity Threats and Defenses2025
  2. 2Adversarial Attacks on AI Systems: A Growing Cyber Threat2023 · 1 citations
  3. 3ARTIFICIAL INTELLIGENCE SAFETY AND ROBUSTNESS AGAINST ADVERSARIAL ATTACKS2026
  4. 4Artificial Intelligence in Cybersecurity: Advancements and Challenges in Data Protection2025
  5. 5Leveraging AI and machine learning for threat detection and adversarial defense in U.S. cybersecurity2025 · 1 citations