@inproceedings{ae064caaddca4fbab3643702f0924eea,
title = "Intrusion detection using geometrical structure",
abstract = "We propose a statistical model, namely Geometrical Structure Anomaly Detection (GSAD) to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile. It calculates weight factor to determine anomaly in the payload. In the 1999 DARPA intrusion detection evaluation data set, we conduct several tests for limited attacks on port 80 and port 25. Our approach establishes and identifies the correlation among packet payloads in a network.",
keywords = "Geometrical structure, Intusion detection, Mahalanobis distance, Pattern recognition, Payload",
author = "Aruna Jamdagni and Zhiyuan Tan and Priyadarsi Nanda and Xiangjian He and Ren Liu",
year = "2009",
doi = "10.1109/FCST.2009.97",
language = "English",
isbn = "9780769539324",
series = "4th International Conference on Frontier of Computer Science and Technology, FCST 2009",
pages = "327--333",
booktitle = "4th International Conference on Frontier of Computer Science and Technology, FCST 2009",
note = "4th International Conference on Frontier of Computer Science and Technology, FCST 2009 ; Conference date: 17-12-2009 Through 19-12-2009",
}