HMMpayl: An Intrusion Detection System Based On Hidden Markov Models

Nowadays the security of Web applications is one of the key topics in Computer Security. Among all the solutions that have been proposed so far, the analysis of the HTTP payload at the byte level has proven to be effective as it does not require the detailed knowledge of the applications running on the Web server. The solutions proposed in the literature actually achieved good results for the detection rate, while there is still room for reducing the false positive rate. To this end, in this paper we propose HMMPayl, an IDS where the payload is represented as a sequence of bytes, and the analysis is performed using Hidden Markov Models (HMM). The algorithm we propose for feature extraction and the joint use of HMM guarantee the same expressive power of n – gram analysis, while allowing to overcome its computational complexity. In addition, we designed HMMPayl following the Multiple Classifiers System paradigm to provide for a better classification accuracy, to increase the difficulty of evading the IDS, and to mitigate the weaknesses due to a non optimal choice of HMM parameters. Experimental results, obtained both on public and private datasets, show that the analysis performed by HMMPayl is particularly effective against the most frequent attacks toward Web applications (such as XSS and SQL-Injection). In particular, for a fixed false positive rate, HMMPayl achieves a higher detection rate respect to previously proposed approaches it has been compared with.