S. Mukkamala, A.H. Sung, Identifying key features for intrusion detection using neural networks, in: Proceedings of the ICCC International Conference on Computer Communications, 2002, pp. 1132--1138.  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, A.H. Sung, Identifying key features for intrusion detection using neural networks, in: Proceedings of the ICCC International Conference on Computer Communications, 2002, pp. 1132--1138.
Intrusion Detection Systems Using Adaptive Regression.. - Mukkamala, Sung, Abraham, .. Self-citation (Mukkamala Sung) (Correct)
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Mukkamala S., Sung A. H., 2002b. Identifying Key Features for Intrusion Detection Using Neural Networks. Proceedings of ICCC International Conference on Computer Communications 2002.
Identifying Significant Features for Network Forensic.. - Mukkamala, Sung (2003) (1 citation) Self-citation (Mukkamala Sung) (Correct)
....an attacker who does not have an account exploits some vulnerability to gain local access and sends packets to the machine over a network. Examples are Dictionary, Ftp write, Guest, Imap, Named, Phf, Sendmail, Xlock, Xsnoop. 3. Ranking The Significance Of Inputs Feature selection and ranking [10,11] is an important issue in network forensics. Of the large number of features that can be monitored for cyber forensics purpose, which are truly useful, which are less significant, and which may be useless The question is relevant because the elimination of useless features (or audit trail ....
....99.78 Table7 Performance of SVMs usin features usin SVDF 34 4.61 0.97 99.55 21 39.69 1.45 99.56 19 73.55 1.50 99.56 23 1.73 0.79 99.87 20 5.94 0.91 99.78 5. Experiments Using Neural Networks This section summarizes the authors recent work in comparing ANNs and SVMs for intrusion detection [4,5,10,11]. Since a (multi layer feedforward) ANN is capable of making multi class classifications, a single ANN (Scaled Conjugate Gradient Decent) is employed to perform the intrusion detection, using the same training and testing sets as those for the SVMs. Neural networks are used for ranking the ....
S Mukkamala, A H. Sung "Identifying Key Features for Intrusion Detection Using Neural Networks," Proceedings of lCCC International Conference on Computer Communications 2002.
Intrusion Detection Systems Using Adaptive Regression Splines - Mukkamala, Sung, Abraham Self-citation (Mukkamala Sung) (Correct)
.... 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 patterns. The data we used in our experiments originated from MIT s Lincoln Lab. It was developed for intrusion detection system ....
S Mukkamala, A H. Sung "Identifying Key Features for Intrusion Detection Using Neural Networks," Proceedings of lCCC International Conference on Computer Communications 2002.
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