On the hardness of evading combinations of linear classifiers
Proceedings of the 2013 ACM workshop on Artificial intelligence and security (AISec '13)
By Daniel Lowd, David Stevens
Issue Date:November 2013
An increasing number of machine learning applications involve detecting the malicious behavior of an attacker who wishes to avoid detection. In such domains, attackers modify their behavior to evade the classifier while accomplishing their goals as efficie...