Open Access
Implementation of two class classifiers for hybrid intrusion detection
M. Arumugam,P. Thangaraj,P. Sivakumar,P. Pradeepkumar +3 more
- pp 486-490
TLDR
In this paper, a multi-stages approach is proposed to enhance the overall performance of IDSs, which is based on a single algorithm that is designed to either model normal behavior patterns or attack signatures in network data traffic.Abstract:
Most intrusion detection systems (IDSs) are based on a single algorithm that is designed to either model normal behavior patterns or attack signatures in network data traffic. Most often, these systems fail to provide adequate alarm capability that reduces false positive and false negative rates. We had proposed multi-stages approaches to enhance the overall performance of IDSs. All models implemented in this paper, must have a perfect 2-classes classifier to differentiate between attacks & normal patterns, so we grant to detect attacks at first stage of IDS and secure the protected system, through other stages we tried to identify the name of intrusion to increase the efficiency of IDS. The first stage is highly capable in detecting normal signature and diverse what-else to attacks category, so it is capable in detecting unseen or unknown attacks. The results of the proposed techniques had shown that a very high increase in the performance of IDS systems. The practical results showed that the multistage system composed of MLP and improved hybrid J48-DT provided the best results among all discussed systems.read more
Citations
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References
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Journal ArticleDOI
The 1999 DARPA off-line intrusion detection evaluation
TL;DR: This report describes new and known approaches and strategies that were used to make attacks stealthy for the 1999 DARPA Intrusion Detection Evaluation, and includes many examples of stealthy scripts that can be use to implement stealthy procedures.
Journal ArticleDOI
Feature deduction and ensemble design of intrusion detection systems
TL;DR: This study investigated the performance of two feature selection algorithms involving Bayesian networks and Classification and Regression Trees and an ensemble of BN and CART and proposed an hybrid architecture for combining different feature selection algorithm for real world intrusion detection.
Proceedings Article
Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99.
TL;DR: To substantiate the performance of machine learning based detectors that are trained on KDD 99 training data, the relevance of each feature is investigated and information gain is employed to determine the most discriminating features for each class.
NIST Special Publication on Intrusion Detection Systems
Rebecca Bace,Peter Mell +1 more
TL;DR: This guidance document is intended as a primer in intrusion detection, developed for those who need to understand what security goals intrusion detection mechanisms serve, how to select and configure intrusion detection systems for their specific system and network environments, and how to integrate intrusion detection functions with the rest of the organizational security infrastructure.
Journal ArticleDOI
Intrusion detection techniques and approaches
Theuns Verwoerd,Ray Hunt +1 more
TL;DR: These techniques including an IDS architectural outline and an analysis of IDS probe techniques finishing with a summary of associated technologies are described.