Adversarial Machine Learning: Attacks From Laboratories to the Real World
Hsiao-Ying Lin, Battista Biggio
Abstract
Adversarial machine learning (AML) is a recent research field that investigates potential security issues related to the use of machine learning (ML) algorithms in modern artificial intelligence (AI)-based systems, along with defensive techniques to protect ML algorithms against such threats. The main threats against ML encompass a set of techniques that aim to mislead ML models through adversarial input perturbations. Unlike ML-enabled crimes, in which ML is used for malicious and offensive purposes, and ML-enabled security mechanisms, in which ML is used for securing existing systems, AML techniques exploit and specifically address the security vulnerabilities of ML algorithms.
Topics & Concepts
Computer scienceOffensiveExploitAdversarial systemAdversarial machine learningComputer securityMachine learningArtificial intelligenceField (mathematics)Set (abstract data type)Operations researchProgramming languagePure mathematicsEngineeringMathematicsAdversarial Robustness in Machine LearningAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications