Article citationsMore>>
S. Staniford-Chen, S. Cheung, R. Crawford, M. Dilger, J. Frank, J. Hoagland, K. Levitt, C. Wee, R. Yip, and D. Zerkle. “GrIDS, a graph based intrusion detection system for large networks,” Proceeding of the 20th National Information Systems Security Conference, Vol. 1, pp. 361– 370, October 1996.
has been cited by the following article:
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TITLE:
Designing Intrusion Detection System for Web Documents Using Neural Network
AUTHORS:
Hari Om, Tapas K. Sarkar
KEYWORDS:
intrusion detection system, neural network, back propagation network, anomaly detection, misuse detection
JOURNAL NAME:
Communications and Network,
Vol.2 No.1,
March
1,
2010
ABSTRACT: Cryptographic systems are the most widely used techniques for information security. These systems however have their own pitfalls as they rely on prevention as their sole means of defense. That is why most of the organizations are attracted to the intrusion detection systems. The intrusion detection systems can be broadly categorized into two types, Anomaly and Misuse Detection systems. An anomaly-based system detects com-puter intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous. Misuse detection systems can detect almost all known attack patterns; they however are hardly of any use to de-tect yet unknown attacks. In this paper, we use Neural Networks for detecting intrusive web documents avail-able on Internet. For this purpose Back Propagation Neural (BPN) Network architecture is applied that is one of the most popular network architectures for supervised learning. Analysis is carried out on Internet Security and Acceleration (ISA) server 2000 log for finding out the web documents that should not be accessed by the unau-thorized persons in an organization. There are lots of web documents available online on Internet that may be harmful for an organization. Most of these documents are blocked for use, but still users of the organization try to access these documents and may cause problem in the organization network.