"Machine Learning: With Great Power Come New Security Vulnerabilities"

There have been many advancements in machine learning (ML) as it has been applied in the operation of self-driving cars, speech recognition, biometric authentication, and more. However, ML models remain vulnerable to a variety of attacks that could lead to the production of incorrect output, posing a threat to safety and security. In order to bolster ML security we should conduct further research on the potential adversaries in ML attacks, the different factors that can influence attackers to target ML systems, and the different ways in which ML attacks can be executed. Using these factors, distinct ML attacks, including evasion, poisoning, and privacy attacks can be identified. This article continues to discuss the importance of understanding why and how ML attacks occur, as well as the structured approach to ML security.

Security Intelligence reports "Machine Learning: With Great Power Come New Security Vulnerabilities"