Gargiulo, Francesco (2009) Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions". [Tesi di dottorato] (Unpublished)


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Item Type: Tesi di dottorato
Language: English
Title: Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions"
Date: 30 November 2009
Number of Pages: 138
Institution: Università degli Studi di Napoli Federico II
Department: Informatica e sistemistica
Doctoral School: Ingegneria dell'informazione
PHD name: Ingegneria informatica ed automatica
PHD cycle: 22
PHD Coordinator:
Date: 30 November 2009
Number of Pages: 138
MIUR S.S.D.: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Date Deposited: 24 May 2010 08:37
Last Modified: 05 Nov 2014 10:58
DOI: 10.6092/UNINA/FEDOA/3894


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.

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