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1. Reliability and Security of AI Hardware

   Gnad, Dennis; Gotthard, Martin; Krautter, Jonas; Kritikakou, Angeliki; Meyers, Vincent; Rech, Paolo; Condia, Josie_Rodriguez; Ruospo, Annachiara; Sanchez, Ernesto; dos_Santos, Fernando_Fernandes; et al (May 2024, IEEE)

   In recent years, Artificial Intelligence (AI) systems have achieved revolutionary capabilities, providing intelligent solutions that surpass human skills in many cases. However, such capabilities come with power-hungry computation workloads. Therefore, the implementation of hardware acceleration becomes as fundamental as the software design to improve energy efficiency, silicon area, and latency of AI systems. Thus, innovative hardware platforms, architectures, and compiler-level approaches have been used to accelerate AI workloads. Crucially, innovative AI acceleration platforms are being adopted in application domains for which dependability must be paramount, such as autonomous driving, healthcare, banking, space exploration, and industry 4.0. Unfortunately, the complexity of both AI software and hardware makes the dependability evaluation and improvement extremely challenging. Studies have been conducted on both the security and reliability of AI systems, such as vulnerability assessments and countermeasures to random faults and analysis for side-channel attacks. This paper describes and discusses various reliability and security threats in AI systems, and presents representative case studies along with corresponding efficient countermeasures. 

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2. AI/ML for Network Security: The Emperor has no Clothes

   Jacobs, Arthur; Beltiukov, Roman; Willinger, Walter; Ferreira, Ronaldo; Gupta, arpit; Granville Lisandro (January 2022, ACM Conference on Computer and Communications Security (CCS))

   Several recent research efforts have proposed Machine Learning (ML)-based solutions that can detect complex patterns in network traffic for a wide range of network security problems. However, without understanding how these black-box models are making their decisions, network operators are reluctant to trust and deploy them in their production settings. One key reason for this reluctance is that these models are prone to the problem of underspecification, defined here as the failure to specify a model in adequate detail. Not unique to the network security domain, this problem manifests itself in ML models that exhibit unexpectedly poor behavior when deployed in real-world settings and has prompted growing interest in developing interpretable ML solutions (e.g., decision trees) for “explaining” to humans how a given black-box model makes its decisions. However, synthesizing such explainable models that capture a given black-box model’s decisions with high fidelity while also being practical (i.e., small enough in size for humans to comprehend) is challenging. In this paper, we focus on synthesizing high-fidelity and low-complexity decision trees to help network operators determine if their ML models suffer from the problem of underspecification. To this end, we present TRUSTEE, a framework that takes an existing ML model and training dataset generate a high-fidelity, easy-to-interpret decision tree, and associated trust report. Using published ML models that are fully reproducible, we show how practitioners can use TRUSTEE to identify three common instances of model underspecification, i.e., evidence of shortcut learning, spurious correlations, and vulnerability to out-of-distribution samples. 

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3. 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. 

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4. 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. 

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5. 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))