Paper
17 March 2008 Intrusion signature creation via clustering anomalies
Author Affiliations +
Abstract
Current practices for combating cyber attacks typically use Intrusion Detection Systems (IDSs) to detect and block multistage attacks. Because of the speed and impacts of new types of cyber attacks, current IDSs are limited in providing accurate detection while reliably adapting to new attacks. In signature-based IDS systems, this limitation is made apparent by the latency from day zero of an attack to the creation of an appropriate signature. This work hypothesizes that this latency can be shortened by creating signatures via anomaly-based algorithms. A hybrid supervised and unsupervised clustering algorithm is proposed for new signature creation. These new signatures created in real-time would take effect immediately, ideally detecting new attacks. This work first investigates a modified density-based clustering algorithm as an IDS, with its strengths and weaknesses identified. A signature creation algorithm leveraging the summarizing abilities of clustering is investigated. Lessons learned from the supervised signature creation are then leveraged for the development of unsupervised real-time signature classification. Automating signature creation and classification via clustering is demonstrated as satisfactory but with limitations.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gilbert R. Hendry and Shanchieh J. Yang "Intrusion signature creation via clustering anomalies", Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730C (17 March 2008); https://doi.org/10.1117/12.775886
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Cited by 20 scholarly publications.
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KEYWORDS
Computer intrusion detection

Evolutionary algorithms

Systems modeling

Binary data

Data modeling

Detection and tracking algorithms

Machine learning

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