Signature-based and protocol-based intrusion detection systems (IDS) are employed as means to reveal content-based network attacks. Such systems have proven to be effective in identifying known intrusion attempts and exploits but they fail to recognize new types of attacks or carefully crafted variants of well known ones. This paper presents the design and the development of an anomaly-based IDS technique which is able to detect content-based attacks carried out over application level protocols, like HTTP and FTP. In order to identify anomalous packets, the payload is split up in chunks of equal length and the n-gram technique is used to learn which byte sequences usually appear in each chunk. The devised technique builds a different model for each pair of protocol of interest and packet length, in terms of number of chunks, and use them to classify the incoming traffic. Models are build by means of an unsupervised approach. Experimental results witness that the technique achieves an excellent accuracy with a very low false positive rate.
Angiulli et al REF studied an unsupervised intrusion detection technique by mining HTTP information using the n-gram model.
PCkAD: an unsupervised intrusion detection technique exploiting within payload n-gram location distribution
Proc. of 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015; Vietri sul Mare, Salerno; Italy. pp. 1093-1098
Computer Science