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Data Science and Machine Learning Enthusiast and Researcher
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Clustering of forest cover type 
Published in Journal of Network Security Computer Networks, 2020
This paper is about improving the classification capabilities of Network Intrusion Detection System (NIDS). Here we experimented with various architectures of DNNs, from smaller/shallow neural networks to more complex networks. To avoid overfitting we made use techniques such as dropout and regularization. For tuning Hyper-parameters we used techniques such as random search, grid search, Hyperband & Bayesian Optimization. Use of these techniques gradually reduces the time required to tune the DNN as well as bounding the validation error.
Recommended citation: D. P. Gaikwad, Amir Mukeri. (2020). "Fine Tuned Deep Neural Networks for Intrusion Detection System." Journal of Network Security Computer Networks. 6(2). http://matjournals.in/index.php/JONSCN/article/view/5143
Published in i-managers Journal on Software Engineering, 2020
In this paper we have applied Support Vector Machine to train Network Intrusion Detection System with and without Principal Component Analysis.
Recommended citation: Mukeri, A., and Gaikwad, D. P. (2020). Support Vector Machine and Principal Component Analysis for Intrusion Detection System. i-managers Journal on Software Engineering , 14(3), 42-49 https://imanagerpublications.com/article/17592/
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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