Cybersecurity for AI Systems: A Survey
Raghvinder S. Sangwan, Youakim Badr, Satish Srinivasan
Abstract
Recent advances in machine learning have created an opportunity to embed artificial intelligence in software-intensive systems. These artificial intelligence systems, however, come with a new set of vulnerabilities making them potential targets for cyberattacks. This research examines the landscape of these cyber attacks and organizes them into a taxonomy. It further explores potential defense mechanisms to counter such attacks and the use of these mechanisms early during the development life cycle to enhance the safety and security of artificial intelligence systems.
Topics & Concepts
Computer scienceComputer securityTaxonomy (biology)Set (abstract data type)Artificial intelligenceData scienceProgramming languageBiologyBotanyAdversarial Robustness in Machine LearningAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection