Mukkamala, S., Janowsky, G., and Sung, A. H. Intrusion Detection using Neural Networks and Support Vector Machines. IEEE International Joint Conference on Neural Networks 2002. Home/Search Document Not in Database Summary Related Articles Check |
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Hybrid Multi-Agent Framework for Detection of Stealthy Probes - Srinivas Mukkamala Andrew Self-citation (Mukkamala Sung) (Correct)
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S. Mukkamala, G. Janoski, A.H. Sung, Intrusion detection using neural networks and support vector machines, in: Proceedings of the IEEE International Joint Conference on Neural Networks, 2002, pp. 1702--1707.
Intrusion Detection Systems Using Adaptive Regression.. - Mukkamala, Sung, Abraham, .. Self-citation (Mukkamala Sung) (Correct)
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Mukkamala S., Janoski G., Sung A. H., 2002a. Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of IEEE International Joint Conference on Neural Networks, pp. 1702-1707.
Designing Intrusion Detection Systems: Architectures.. - Mukkamala, Sung, Abraham Self-citation (Mukkamala Sung) (Correct)
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S Mukkamala, G Janowski, A H. Sung. (2001) Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of Hybrid Information Systems Advances in Soft Computing, Physica Verlag, Springer Verlag, ISBN 3790814806, pp.121-138.
Identifying Significant Features for Network Forensic.. - Mukkamala, Sung (2003) (1 citation) Self-citation (Mukkamala Sung) (Correct)
....and misuse patterns, which requires human intervention. In our recent work on offiine intrusion analysis, artificial intelligence techniques are developed to automate the process by reducing human intervention. SVMs are found to be superior to ANNs in many important respects of intrusion detection [4,5]. In this paper we will concentrate on SVMs and briefly summarize the results of ANNs. The data we used in our experiments originated from MIT s Lincoln Labs. It was developed for offiine intrusion detection system evaluations by DARPA and is considered a benchmark for intrusion detection ....
.... and not infallible (since the available data may be of poor quality in sampling the whole input space) In the following, therefore, we apply the technique of deleting one feature at a time to rank the input features and identify the most important ones for intrusion detection using SVMs [4,5]. 3.1 Performance Based Method for Ranking Importance We first describe a general (i.e. independent of the modeling tools being used) performancebased input ranking methodology: One input feature is deleted from the data at a time; the resultant data set is then used for the training and ....
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Mukkamala S., Janoski G., Sung A. H. (2002) "Intrusion Detection Using Neural Networks and Support Vector Machines," Proceedings of lEEE International Joint Conference on Neural Networks, pp. 1702-1707.
Intrusion Detection Using Ensemble of Soft Computing.. - Mukkamala, Sung, Abraham (2004) Self-citation (Mukkamala Sung) (Correct)
....soft computing techniques and also their ensemble for building models based on experimental data. Since most of the intrusions can be uncovered by examining pattems of user activities, many IDSs have been built by utilizing the recognized attack and misuse pattems to develop learning machines [3,4,5,6,7,8,9]. In our recent work, SVMs are found to be superior to ANNs in many important respects of intrusion detection [9] In this paper we will concentrate on using the ensemble of support vector machines and neural networks with different training functions to achieve better classification accuracies. ....
.... can be uncovered by examining pattems of user activities, many IDSs have been built by utilizing the recognized attack and misuse pattems to develop learning machines [3,4,5,6,7,8,9] In our recent work, SVMs are found to be superior to ANNs in many important respects of intrusion detection [9]; In this paper we will concentrate on using the ensemble of support vector machines and neural networks with different training functions to achieve better classification accuracies. The data we used in our experiments originated from MIT s Lincoln Lab. It was developed for intrusion detection ....
Mukkamala S., Janoski G., Sung A. H. (2002) "intrusion Detection Using Neural Networks and Support Vector Machines," Proceedings of IEEE International Joint Conference on Neural Networks, pp. 1702-1707.
Intrusion Detection Systems Using Adaptive Regression Splines - Mukkamala, Sung, Abraham Self-citation (Mukkamala Sung) (Correct)
....(mostly) real time intrusion detection is the goal of the researchers in IDSs. Various artificial intelligence techniques have been utilized to automate the intrusion detection process to reduce human intervention; several such techniques include neural networks [3,4,5,6,7] and machine learning [8]. Several data mining techniques have been introduced to identify key features or parameters that define intrusions [9,10,11,12] In this paper, we explore Multivariate Adaptive Regression Splines (MARS) SVMs and neural networks, to perform intrusion detection based on recognized attack ....
Mukkamala S., Janoski G., Sung A. H. (2002) "Intrusion Detection Using Neural Networks and Support Vector Machines," Proceedings of lEEE International Joint Conference on Neural Networks, pp. 1702-1707.
A Multiagent Approach to Outbound Intrusion Detection - Mandujano (2004) (Correct)
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Mukkamala, S., Janowsky, G., and Sung, A. H. Intrusion Detection using Neural Networks and Support Vector Machines. IEEE International Joint Conference on Neural Networks 2002.
Intrusion Detection Systems Using Decision Trees and.. - Sandhya.. (Correct)
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S. Mukkamala, G. Janoski, A. Sung. Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of IEEE International Joint Conference n Neural Networks, pp.1702-1707, 2002
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