More Like this

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

   https://doi.org/10.1109/COMPSAC57700.2023.00284  

   Rahman, Md Mostafizur ; Siddika Arshi, Aiasha ; Hasan, Md Mehedi ; Farzana Mishu, Sumayia ; Shahriar, Hossain ; Wu, Fan ( June 2023 , 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC))

   This survey paper provides an overview of the current state of Artificial Intelligence (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. 

   more » « less
2. Security Risk and Attacks in AI: A Survey of Security and Privacy

   Md Mostafizur Rahman, Aiasha Siddika ( June 2023 , Proc. of the 17th IEEE International Workshop on Security, Trust, and Privacy for Software Applications)

   This survey paper provides an overview of the current state of Artificial Intelligence (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. 

   more » « less
3. Towards the Security of AI-Enabled UAV Anomaly Detection

   https://doi.org/10.1109/ICC45041.2023.10279224  

   Raja, Ashok ; Jia, Mengjie ; Yuan, Jiawei ( May 2023 , IEEE International Conference on Communications)
4. Towards AI-Enabled Hardware Security: Challenges and Opportunities

   https://doi.org/10.1109/IOLTS56730.2022.9897507  

   Sayadi, Hossein ; Aliasgari, Mehrdad ; Aydin, Furkan ; Potluri, Seetal ; Aysu, Aydin ; Edmonds, Jack ; Tehranipoor, Sara ( September 2022 , 2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS))
5. TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

   https://doi.org/10.1109/JIOT.2021.3122019  

   Zolanvari, Maede ; Yang, Zebo ; Khan, Khaled ; Jain, Raj ; Meskin, Nader ( October 2021 , IEEE Internet of Things Journal)

   Despite AI’s significant growth, its “black box” nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in high-risk applications. Explainable AI (XAI) has emerged to help with this problem. Designing effectively fast and accurate XAI is still challenging, especially in numerical applications. We propose a novel XAI model named Transparency Relying Upon Statistical Theory (TRUST) for XAI. TRUST XAI models the statistical behavior of the underlying AI’s outputs. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these parameters and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then we use multi-model Gaussian distributions to determine the likelihood of any new sample belonging to each class. The proposed technique is a surrogate model that is not dependent on the type of the underlying AI. TRUST is suitable for any numerical application. Here, we use cybersecurity of the industrial internet of things (IIoT) as an example application. We analyze the performance of the model using three different cybersecurity datasets, including “WUSTLIIoT”, “NSL-KDD”, and “UNSW”. We also show how TRUST is explained to the user. The TRUST XAI provides explanations for new random samples with an average success rate of 98%. Also, the advantages of our model over another popular XAI model, LIME, including performance, speed, and the method of explainability are evaluated. 

   more » « less