首页|University of Queensland Details Findings in Machine Learning (Evasion Attack an d Defense On Machine Learning Models In Cyberphysical Systems: a Survey)
University of Queensland Details Findings in Machine Learning (Evasion Attack an d Defense On Machine Learning Models In Cyberphysical Systems: a Survey)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Brisbane, Australia, by NewsRx co rrespondents, research stated, "Cyber-physical systems (CPS) are increasingly re lying on machine learning (ML) techniques to reduce labor costs and improve effi ciency. However, the adoption of ML also exposes CPS to potential adversarial ML attacks witnessed in the literature." Our news journalists obtained a quote from the research from the University of Q ueensland, "Specifically, the increased Internet connectivity in CPS has resulte d in a surge in the volume of data generation and communication frequency among devices, thereby expanding the attack surface and attack opportunities for ML ad versaries. Among various adversarial ML attacks, evasion attacks are one of the most well-known ones. Therefore, this survey focuses on summarizing the latest r esearch on evasion attack and defense techniques, to understand state-of-the-art ML model security in CPS. To assess the attack effectiveness, this survey propo ses an attack taxonomy by introducing quantitative measures such as per-turbation level and the number of modified features. Similarly, a defense taxonomy is int roduced based on four perspectives demonstrating the defensive techniques from m odels' inputs to their outputs."
BrisbaneAustraliaAustralia and New Z ealandCyborgsEmerging TechnologiesMachine LearningUniversity of Queensla nd
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