Machine Learning Security and Trustworthiness
Jody Booth, Werner A. Metz, Anahit Tarkhanyan, Sunil Cheruvu · Apress eBooks · 2023
Machine Learning (ML) is the innovation powerhouse for Intelligent Multimodal Security Systems (IMSS). Along with obvious benefits, ML brings unique risks that require thorough assessment. ML system security builds on "traditional" cybersecurity controls and spreads to cover the expanded attack surfaces for the new ML assets. Moreover, the problem of ML trustworthiness stands in the way of taking full advantage of ML advancements. This chapter introduces the challenges and risks of ML, highlights key trends in the global ML policies, and best practices, summarizes key standardization activities, and provides a detailed description of assets and threats. Putting the above into perspective, we present a practical framework for addressing ML-based IMSS security and trustworthiness and discuss relevant implementation options.