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Network Anomaly Detection and User Behavior Analysis using Machine Learning

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Priti H. Vadgaonkar
10.5120/ijca2020920635

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

@article{10.5120/ijca2020920635,
	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 = {http://www.ijcaonline.org/archives/volume175/number13/31517-2020920635},
	doi = {10.5120/ijca2020920635},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

  1. S. P. Shashikumar, A. J. Shah, Q. Li, G. D. Clifford, and S. Nemati, “A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology,” in Proc. IEEE EMBS Int. Conf. Biomed. Health Informat, FL, USA, 2017, pp. 141–144.
  2. K. Kostas, "Anomaly Detection in Networks Using Machine Learning", Research Proposal, march 2018, pp. 1-64.
  3. K. Leung and C. Leckie, “Unsupervised anomaly detection in network intrusion detection using clusters”, Proceedings of the Twenty-eighth Australasian conference on Computer Science,2005, pp. 333-342.
  4. I. Sharafaldin, A. Gharib, A. H. Lashkari, and A. A. Ghorbani, "Towards a reliable intrusion detection benchmark dataset", Software Networking, 2017, pp. 177-200.
  5. Sonali Naikade, Akshaya Ramaswamy, Burhan Sadliwala, Prof. Dr. Pravin Futane Atmaja Sahasrabuddhel," Survey on Intrusion Detection System using Data Mining Techniques", International Research Journal of Engineering and Technology, may 2017, pp. 1780-1784.
  6. B. Dong and X. Wang," Comparison deep learning method to traditional methods using for network intrusion detection", Proc. 8th IEEE Int.Conf. Commun. Softw. Netw., Beijing, China, june 2016, pp. 581–585.
  7. R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao,, "deep learning and its applications to machine health monitoring: A survey", Submitted to IEEE Trans. Neural Netw. Learn. Syst, 2016, pp. 1-14.
  8. Purdy, K. A., “Toward an online anomaly intrusion detection system based on deep learning", in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl., Anaheim, CA, USA, Dec 2016, pp. 195–200.
  9. S. Hou, S. Hou, A. Saas, L. Chen, and Y. Ye, " Deep4MalDroid: A Deep learning framework for android malware detection based on linux kernel system call graphs", in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Workshops,Omaha, NE, USA,Oct 2016, pp. 104–111.
  10. L. You, Y. Li, Y. Wang, J. Zhang, and Y. Yang , " A deep learning based RNNs model for automatic security audit of short messages", in Proc. 16th Int. Symp. Commun. Inf. Technol., Qingdao, China, sept 2016, pp. 225–229.
  11. S. Potluri and C. Diedrich," Accelerated deep neural networks for enhanced intrusion detection system", in Proc. IEEE 21st Int.Conf. Emerg. Technol. Factory Autom., Berlin, Germany, sept 2016, pp. 1–8.
  12. M.-J. Kang and J.-W. Kang, "Intrusion detection system using deep neural network for in-vehicle network security", PLoS One, june 2016.
  13. Q. Niyaz, W. Sun, and A. Y. Javaid, "A deep learning based DDOS detection system in software-defined networking (SDN)", Submitted to EAI Endorsed Transactions on Security and Safety, 2017.
  14. H.-W. Lee, N.-R. Kim, and J.-H. Lee, "Deep neural network self-training based on unsupervised learning and dropout", Int. J. Fuzzy Logic Intell Syst, Mar 2017, pp. 1-9.
  15. L.Deng, "Deep learning: Methods and applications", Found. Trends Signal Process, Aug. 2014, pp. 197–387.
  16. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks", Science, 2006, pp. 504–507.
  17. Davis J.J., Clark A.J., " Data preprocessing for anomaly based network intrusion detection", Computer & Security, 2011, pp. 353-375.
  18. SomanK.P. DiwakarS., AjayV, “Insight into Data Mining Theory and Practice”, PHI Learning Pvt Ltd, Third edition (2008).
  19. Sumathi S., Sivanandam S.N., “Data mining in security”, Studies in Computational Intelligence (SCI), Springer 2006, pp. 629 -648.
  20. Neethu B., “Classification of Intrusion Detection Dataset using machine learning Approaches”, International Journal of Electronics and Computer Science Engineering, 2012, pp. 1044-1051.
  21. L. Breiman, “Random forests,” Mach. Learn., 2001, pp. 5–32.
  22. Nathan Shone , Tran Nguyen Ngoc, Vu Dinh Phai , and Qi Shi, N., "A Deep Learning Approach to Network intrusion detection", ieee transactions on emerging topics in computational intelligence, Feb. 2018, pp. 41-50.
  23. I. Goodfellow, Y. Bengio, and A. Courville,”Deep Learning”, Cambridge, MA, USA: MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org

Keywords

Anomaly detection, deep learning, auto encoder, PCA.