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Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic

Song, Yingbo; Keromytis, Angelos D.; Stolfo, Salvatore

We present Spectrogram, a mixture of Markov-chains sensor for anomaly detection (AD) against web-layer (port 80) code-injection attacks such as PHP file inclusion, SQL-injection, cross-site-scripting, as well as memory layer buffer overflows. Port 80 is the gateway to many application level services and a large array of attacks are channeled through this vector, servers cannot easily firewall this port. Signature-based sensors are effective in filtering known exploits but cannot detect 0-day vulnerabilities or deal with polymorphism and statistical AD approaches have mostly been limited to network layer, protocol-agnostic modeling, weakening their effectiveness. N -gram based modeling approaches have recently demonstrated success but the ill-posed nature of modeling large grams have thus far prevented exploration of higher order statistical models. In this paper, we provide a solution to this problem based on a factorization into Markov-chains and aim to model higher order structure as well as content for web requests. Spectrogram is implemented in a protocol-aware, passive, network-situated, but CGI-layered, AD architecture and we show in our evaluation that this model demonstrates significant detection results on an array of real world web-layer attacks, achieving at least 97% detection rates on all but one dataset and comparing favorably against other AD sensors.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-040-08
Published Here
April 26, 2011
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