The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

Understand

Machine learning models have made many decision support systems to be faster, more accurate, and more efficient.

• However, applications of machine learning in network security face a more disproportionate threat of active adversarial attacks compared to other domains.
• This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature.
• In what could be considered an arm's race between attackers and defenders, adversaries constantly probe machine learning systems with inputs that are explicitly designed to bypass the system and induce a wrong prediction.