ACAS: automated construction of application signatures

By Patrick Haffner, Subhabrata Sen, Oliver Spatscheck, Dongmei Wang. Annual ACM Workshop on Mining Network Data, 2005

431 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/48fc90e1a20d1327763b1f65f61d5a49209919d9

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In Artificial Intelligence

In Computer Networks and Communications

In Information Systems

Citations in the corpus

A survey of techniques for internet traffic classification using machine learning (2008, methodology): “The approach does not require the classifier to capture the start of each traffic flow (as required in [53] and [57]).”
Traffic classification using clustering algorithms (2006, methodology): “To address the aforementioned drawbacks of port-based classification, several payload-based analysis techniques have been proposed [3, 6, 9, 11, 15].”
Polyglot: automatic extraction of protocol message format using dynamic binary analysis (2007, methodology): “• New techniques for extracting protocol keywords: Current techniques for extracting keywords find protocol keywords by looking for tokens that occur repeatedly at the same position in multiple messages [17, 21, 25].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Application signatures are built using features, such as average packet size, flow duration, bytes per flow, packets per flow, root mean square packet size and IP traffic packet payload [176, 390].”
Network Traffic Classification Using Correlation Information (2013, background): “Machine learning can automatically search for and describe useful structural patterns in a supplied traffic data set, which is helpful to intelligently conduct traffic classification [8], [7].”
A Survey on Internet Traffic Identification (2009, methodology): “To ease the manual signature construction process, some approaches focus on extracting application signatures from IP traf ﬁ c payload content automatically [47][48][49].”
Internet traffic classification demystified: myths, caveats, and the best practices (2008, methodology): “A more reliable approach adopted by commercial tools [2, 3] inspects packet payloads for specific string patterns of known applications [12, 22 , 25, 30, 37].”
Offline/realtime traffic classification using semi-supervised learning (2007, background): “Studies show that these approaches work very well for today’s Internet traffic, including P2P flows [15,28].”