Adversarial Machine Learning in Sensors: Attacks, Defenses and Outlooks

Dear Colleagues,

Machine learning has become a critical part of society, used in forecasting, autonomous vehicles, critical infrastructure, healthcare and even surveillance. However, machine learning algorithms are vulnerable to attacks during training and testing time, which can lead to  their confidentiality, integrity, and availability being harmed. Although many defences have been proposed in the past, the issue remains largely unsolved, as even state-of-the-art defences can be evaded by attackers.

In this Special Issue, we welcome any papers that explore or identify vulnerabilities in machine learning and papers which propose robust defences. Special attention will be given to papers which explore the issue of evadable defences and provide insights into how defences can create more resilient attackers.

Dr. Yisroel Mirsky
Guest Editor

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