Open Access   Article Go Back

A Semi-supervised Approach for Abnormal User Behaviour Detection in Network

Nandit Malviya1 , Mukta S. Takalikar2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-8 , Page no. 25-29, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i8.2529

Online published on Aug 31, 2018

Copyright © Nandit Malviya, Mukta S. Takalikar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Nandit Malviya, Mukta S. Takalikar, “A Semi-supervised Approach for Abnormal User Behaviour Detection in Network,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.25-29, 2018.

MLA Style Citation: Nandit Malviya, Mukta S. Takalikar "A Semi-supervised Approach for Abnormal User Behaviour Detection in Network." International Journal of Computer Sciences and Engineering 6.8 (2018): 25-29.

APA Style Citation: Nandit Malviya, Mukta S. Takalikar, (2018). A Semi-supervised Approach for Abnormal User Behaviour Detection in Network. International Journal of Computer Sciences and Engineering, 6(8), 25-29.

BibTex Style Citation:
@article{Malviya_2018,
author = {Nandit Malviya, Mukta S. Takalikar},
title = {A Semi-supervised Approach for Abnormal User Behaviour Detection in Network},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2018},
volume = {6},
Issue = {8},
month = {8},
year = {2018},
issn = {2347-2693},
pages = {25-29},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2649},
doi = {https://doi.org/10.26438/ijcse/v6i8.2529}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.2529}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2649
TI - A Semi-supervised Approach for Abnormal User Behaviour Detection in Network
T2 - International Journal of Computer Sciences and Engineering
AU - Nandit Malviya, Mukta S. Takalikar
PY - 2018
DA - 2018/08/31
PB - IJCSE, Indore, INDIA
SP - 25-29
IS - 8
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
703 419 downloads 273 downloads
  
  
           

Abstract

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are generic. Detecting abnormal user behavior is of great significance for a secured network. The traditional detection method, which is based on machine learning, usually needs to accumulate a large amount of abnormal behaviour data from different times or even different network environments for training, so the data gathered is not in line with practical data and thus affects. There are many systems being developed which analyzes big data logs and recognizes patterns in it with already predefined classes using machine learning algorithm. The current research in this area implements algorithm like SVM (support vector machines), PCA (principal component analysis) mostly to classify data. Apart from this many are working to find different classes to classify anomalous activities. In this project, analysis of various machine learning algorithms will be carried out irrespective of user behaviour.

Key-Words / Index Term

Anomaly Detection, Learning process, Machine Learning, Security

References

[1] You Lu, Xuefeng Xi, Ze Hua, Hongjie Wu, Ni Zhang “An abnormal user behavior detection method based on partially labelled data” Computer Modelling New Technologies, pp.132-141,March 2014.
[2] Bi M, Xu J, Wang M, Zhou F. "Anomaly detection model of user behavior based on principal component analysis". Journal of Ambient Intelligence and Humanized Computing, pp.547-554, August 2016.
[3] Khurum Nazir Junejo, Jonathan Goh, “Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning”, CPSS,ACM, 2016.
[4] Hanumantha Rao, G. Srinivas, Ankam Damodhar and M. Vikas Krishna “Implementation of Anomaly Detection Technique Using Machine Learning Algorithms”, International Journal of Computer Science and Telecommunications, Volume 2, Issue 3, June 2011.
[5] Pajouh HH, Dastghaibyfard G, Hashemi S. "Two-tier network anomaly detection model: a machine learning approach". Journal of Intelligent Information Systems, pp.61-74, Feb 2017.
[6] Pandeeswari N, Kumar G. "Anomaly detection system in cloud environment using fuzzy clustering based ANN". Mobile Networks and Applications, pp.494-505, Jun 2016 .
[7] Deepaa A J, Kavitha V "A Comprehensive Survey on Approaches to Intrusion Detection System", Procedia Engineering, pp.2063-9, 2012.
[8] Kloft M, Brefeld U, Duessel P, Gehl C, Laskov P. “Automatic feature selection for anomaly detection”, Proceedings of the 1st ACM workshop on Workshop on AISec. ACM, 2008.
[9] Tsang IW, Kwok JT, Cheung PM. "Core vector machines: Fast SVM training on very large data sets". Journal of Machine Learning Research, pp.363-9, 2005.
[10] Khan L, Award M, Thuraisingham B "A new intrusion detection system using support vector machines and hierarchical clustering". VLDB Journal, pp.507-21 2007.
[11] Mitchell, R. and Chen, R., "Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems", IEEE Transactions on Dependable and Secure Computing, pp.16-30, 2015.
[12] Jaime Devesa, Igor Santos, Xabier Cantero, Yoseba K. Penya and Pablo G. Bringas "Automatic behaviour-based Analysis and Classification System for Malware Detection”,Deusto Technological Foundation, Bilbao, Spain 2010.
[13] Teng, Shaohua, Naiqi Wu, Haibin Zhu, Luyao Teng, and Wei Zhang. "SVM-DT-based adaptive and collaborative intrusion detection", IEEE/CAA Journal of Automatica Sinica, pp.108-118, 2018.
[14] Yao, H., Y. Liu, and C. Fang, “An Abnormal Network Traffic Detection Algorithm Based on Big Data Analysis”.International Journal of Computers,Communications Control, 2016.
[15] Hsieh, C.-J. and T. Y. Chan. "Detection DDoS attacks based on neural network using Apache Spark,International Conference in Applied System Innovation, 2016.
[16] Ambusaidi MA, He X, Nanda P, Tan Z., “Building an intrusion detection system using a filter-based feature selection algorithm”. IEEE transactions on computers, pp.2986-98, 2016.
[17] Meng Jiang and Peng Cui, Christos Faloutsos, "Suspicious Behavior Detection: Current Trends and Future Directions”, IEEE Computer Society, January/February 2016.
[18] Thomas Dietterich, Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, "Adaptive Computation and Machine Learning" MIT press, 2011.
[19] Stephen D. Bay and Dennis F. Kibler and Michael J. Pazzani and Padhraic Smyth, "The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation", SIGKDD Explorations, 2000.