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10 results on '"Lalmuanawma, Samuel"'

1. Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers

Author

Dawngliani, M. S., Chandrasekaran, N., Lalmuanawma, Samuel, Thangkhanhau, H., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jain, Lakhmi C., editor, Peng, Sheng-Lung, editor, and Wang, Shiuh-Jeng, editor

Published
2020
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3. Fusion of Misuse Detection with Anomaly Detection Technique for Novel Hybrid Network Intrusion Detection System

Author

Hussain, Jamal, Lalmuanawma, Samuel, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Patnaik, Srikanta, editor, and Popentiu-Vladicescu, Florin, editor

Published
2017
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10. A Hybrid Approach for Determining the Efficient Network Intrusion Detection System.

Author

Hussain, Jamal and Lalmuanawma, Samuel

Subjects
COMPUTER networks, MATHEMATICAL programming, HYBRID systems, INTRUSION detection systems (Computer security), SCIENTIFIC experimentation
Abstract

The risk of intrusion is increasing with the increasing computer networks. To prevent such intrusion, Network Intrusion Detection System (NIDS) is used. The earlier models of NIDS were misuse or anomaly-based and used individual classification technique. However, those systems failed to provide complete protection due to the evolution of different new attack patterns, forcing researchers towards a new era of classification techniques using combinations of multiple classification algorithm called hybrid intelligent system, resulting in more detection accuracy. In this paper, we test various kinds of hybrid intelligent systems and propose the best model which scores the highest accuracy throughout the experimentation. We use 5-class (i.e., Normal, Dos, U2R, R2L and Probe) classification strategies along with k-fold cross validation. NSL-KDD dataset, which is an improved version of KDD99 dataset, is used for evaluation and experimentation of our proposed hybrid NIDS. [ABSTRACT FROM AUTHOR]

Published
2014

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