A Supervised Classification Approach for Detecting Packets Originated in a HTTP-based Botnet
Author(s) -
Félix Brezo,
José Gaviria de la Puerta,
Xabier Ugarte-Pedrero,
Igor Santos,
Pablo G. Bringas,
David Barroso
Publication year - 2013
Publication title -
clei electronic journal
Language(s) - English
Resource type - Journals
ISSN - 0717-5000
DOI - 10.19153/cleiej.16.3.2
Subject(s) - botnet , malware , computer science , network packet , traffic analysis , volume (thermodynamics) , artificial intelligence , data mining , machine learning , computer network , computer security , world wide web , the internet , physics , quantum mechanics
The possibilities that the management of a vast amount of computers and/or networks oer is attracting an increasing number of malware writers. In this document, the authors propose a methodology thought to detect malicious botnet trac, based on the analysis of the packets that ow within the network. This objective is achieved by means of the extraction of the static characteristics of packets, which are lately analysed using supervised machine learning techniques focused on trac labelling so as to proactively face the huge volume of information nowadays lters work with.
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