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Network Anomaly Detection and User Behavior Analysis using Machine Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Priti H. Vadgaonkar

Priti H Vadgaonkar. Network Anomaly Detection and User Behavior Analysis using Machine Learning. International Journal of Computer Applications 175(13):47-53, August 2020. BibTeX

	author = {Priti H. Vadgaonkar},
	title = {Network Anomaly Detection and User Behavior Analysis using Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2020},
	volume = {175},
	number = {13},
	month = {Aug},
	year = {2020},
	issn = {0975-8887},
	pages = {47-53},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2020920635},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Millions of people and hundreds of thousands of institutions communicate with each other over the Internet every day. In the past two decades, while the number of users using the Internet has increased very rapidly. Align to these developments, the number of attacks made on the Internet is increasing day by day. Although signature-based detection methods are used to avert these attacks, they are failed against zero-day attacks. In this study, the focus is to detect network anomaly using machine learning methods. For the implementation of proposed classifier, the graphics processing unit (GPU)-enabled TenserFlow will be used and for evaluation purpose the benchmark KDD Cup 99 and NSL-KDD datasets will be used for its wide attack diversity.On this dataset, several different machine learning algorithms will be trained and tested to make the model robust and accurate.


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Anomaly detection, deep learning, auto encoder, PCA.