Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions"

Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc. A classical example of such "adversarial" environment are various evasion techniques used in generation of spam emails. Similar problems arise in web search (web spam) and malware analysis (obfuscation and polymorphism). The underlying problem is that pattern recognition, as well as data analysis techniques in general, have not been designed to work in adversarial environments. This consideration arise with the problem to define a general framework to prevents this kind of evasions. In this thesis we propose some techniques to approach with the "adversarial" environments. We first present a novel multiple classifier systems approach, called SOCIAL, and then we will show some methodologies applied to different applications such as the spam detection and the traffic identification.