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Security Risk and Attacks in AI: A Survey of Security and Privacy
Conference proceeding   Peer reviewed

Security Risk and Attacks in AI: A Survey of Security and Privacy

Md Mostafizur Rahman, Aiasha Siddika Arshi, Md Mehedi Hasan, Sumayia Farzana Mishu, Hossain Shahriar and Fan Wu
2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Vol.2023-, pp.1834-1839
Annual Computers, Software, and Applications Conference (COMPSAC), 47th (Torino, Italy, 06/26/2023–06/30/2023)
08/2023
DOI: https://doi.org/10.1109/COMPSAC57700.2023.00284
Web of Science ID: WOS:001046484100274

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Abstract

Adversarial attacks Privacy Secure machine learning Security Artificial Intelligence or Cybernetics Cybersecurity Machine Learning
This survey paper provides an overview of the current state of AI attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques.

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