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


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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions"
Data: 30 Novembre 2009
Numero di pagine: 138
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Informatica e sistemistica
Scuola di dottorato: Ingegneria dell'informazione
Dottorato: Ingegneria informatica ed automatica
Ciclo di dottorato: 22
Coordinatore del Corso di dottorato:
Data: 30 Novembre 2009
Numero di pagine: 138
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Depositato il: 24 Mag 2010 08:37
Ultima modifica: 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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