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1. ADMM attack: an enhanced adversarial attack for deep neural networks with undetectable distortions

   https://doi.org/10.1145/3287624.3288750  

   Zhao, Pu; Xu, Kaidi; Liu, Sijia; Wang, Yanzhi; Lin, Xue (January 2019, Asia and South Pacific Design Automation Conference (ASP-DAC) 2019)
2. Employing attack graphs for intrusion detection

   https://doi.org/10.1145/3368860.3368862  

   Capobianco, Frank; George, Rahul; Huang, Kaiming; Jaeger, Trent; Krishnamurthy, Srikanth; Qian, Zhiyun; Payer, Mathias; Yu, Paul (September 2019, New Security Paradigms Workshop)

   Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must examine new perspectives for intrusion detection. Current intrusion detection systems focus on known attacks and/or vulnerabilities, limiting their ability to identify new attacks, and lack the visibility into all system components necessary to confirm attacks accurately, particularly programs. To change the landscape of intrusion detection, we propose that future IDSs track how attacks evolve across system layers by adapting the concept of attack graphs. Attack graphs were proposed to study how multi-stage attacks could be launched by exploiting known vulnerabilities. Instead of constructing attacks reactively, we propose to apply attack graphs proactively to detect sequences of events that fulfill the requirements for vulnerability exploitation. Using this insight, we examine how to generate modular attack graphs automatically that relate adversary accessibility for each component, called its attack surface, to flaws that provide adversaries with permissions that create threats, called attack states, and exploit operations from those threats, called attack actions. We evaluate the proposed approach by applying it to two case studies: (1) attacks on file retrieval, such as TOCTTOU attacks, and (2) attacks propagated among processes, such as attacks on Shellshock vulnerabilities. In these case studies, we demonstrate how to leverage existing tools to compute attack graphs automatically and assess the effectiveness of these tools for building complete attack graphs. While we identify some research areas, we also find several reasons why attack graphs can provide a valuable foundation for improving future intrusion detection systems. 

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3. Attack Recovery for Cyber-Physical Systems

   Kong, Fanxin; Wang, Zifan (October 2023, EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles)

   Cyber-physical systems (CPSs) rely on computing components to control physical objects, and have been widely used in real-world life-critical applications. However, a CPS has security risks by nature due to the integration of many vulnerable subsystems, which adversaries exploit to inflict serious consequences. Among various attacks, sensor attacks pose a particularly significant threat, where an attacker maliciously modifies sensor measurements to drift system behavior. There is a lot of work in sensor attack prevention and detection. Nevertheless, an essential problem is overlooked: recovery--what to do after detecting a sensor attack, which needs to safely and timely bring a CPS back. We aim to highlight the need to investigate this problem, outline its four key challenges, and provide a brief overview of initial solutions in the field. 

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4. Attack Some while Protecting Others: Selective Attack Strategies for Attacking and Protecting Multiple Concepts

   https://doi.org/10.1145/3576915.3623177  

   Belavadi, Vibha; Zhou, Yan; Kantarcioglu, Murat; Thuraisingham, Bhavani (November 2023, ACM)
5. Attack of the Knights:Non Uniform Cache Side Channel Attack

   https://doi.org/10.1145/3627106.3627199  

   Mahmud, Farabi; Kim, Sungkeun; Chawla, Harpreet Singh; Kim, Eun Jung; Tsai, Chia-Che; Muzahid, Abdullah (December 2023, ACM)