Machine Learning in Adversarial Environments for Computer Security

Computer and network security has become an important research area due to the alarming recent increase in hacker activity motivated by profit and both ideological and national conflicts. Increases in spam, botnets, viruses, malware, key loggers, software vulnerabilities, zero-day exploits and other threats contribute to growing concerns about security. In the past few years, many researchers have begun to apply machine learning techniques to these and other security problems. Security, however, is a difficult area because adversaries actively manipulate training data and vary attack techniques to defeat new systems. A main purpose of this workshop is examine adversarial machine learning problems across different security applications to see if there are common problems, effective solutions, and theoretical results to guide future research, and to determine if machine learning can indeed work well in adversarial environments. Another purpose is to initiate a dialog between computer security and machine learning researchers already working on various security applications, and to draw wider attention to computer security problems in the NIPS community.