Anomaly Detection in Data Mining using Fuzzy C-Means Technique and Artificial Neural Network

Jagruti D. Parmar, Jalpa T. Patel

Abstract


Anomaly detection is the new research topic to this new generation researcher in present time. Anomaly detection is a domain i.e., the key for the upcoming data mining. The term ‘data mining’ is referred for methods and algorithms that allow extracting and analyzing data so that find rules and patterns describing the characteristic properties of the information. Techniques of data mining can be applied to any type of data to learn more about hidden structures and connections. In the present world, vast amounts of data are kept and transported from one location to another. The data when transported or kept is informed exposed to attack. Though many techniques or applications are available to secure data, ambiguities exist. As a result to analyze data and to determine different type of attack data mining techniques have occurred to make it less open to attack. Anomaly detection is used the techniques of data mining to detect the surprising or unexpected behaviour hidden within data growing the chances of being intruded or attacked. This paper work focuses on Anomaly Detection in Data mining. The main goal is to detect the anomaly in time series data using machine learning techniques.

Full Text:

PDF

References


J. Huysmans, B. Baesens, D. Martens, K. Denys And J. Vanthienen, “New Trends in Data Mining”, Tijdschrift voor Economie en Management, Vol. L, 4, 2005: 1-14.

Varun Chandola, Arindam Banerjee and Vipin Kumar, “Anomaly Detection: A Survey”, ACM Computing Surveys, Vol. 41, No. 3, Article 15, 2009: 1-58.

Animesh Patcha, Jung-Min Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends”, ScienceDirect 2007.

H. Debar, M. Dacier, and A. Wespi, “A revised taxonomy for intrusion-detection systems,” In Annales des telecommunications, Springer-Verlag, vol. 55, no. 7-8, pp. 361-378, 2000.

A.S. Ashoor and S. Gore, “Importance of Intrusion Detection system (IDS),” International Journal of Scientific and Engineering Research, vol. 2, no. 1, pp. 1-4, 2011.

Kalyani M Raval, “Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Volume 2, Issue 10, 2012: 439-442.

P.G. Majeed and S. Kumar, “Genetic algorithms in intrusion detection systems: A survey,” International Journal of Innovation and Applied Studies, vol. 5, no. 3, pp. 233-236, 2014.

F. Alserhani, M. Akhlaq, I. U. Awan, A. J. Cullen, J. Mellor, and P. Mirchandani, “Snort Performance Evaluation,” In Proceedings of Twenty Fifth UK Performance Engineering Workshop (UKPEW 2009), Leeds, UK, 2009.

Philippe Esling and Carlos Agon, ”Time-Series Data Mining”, ACM Computing Surveys, Volume 45, No. 1, Article 12 (2012),: 1- 34.

Varun Chandola, Deepthi Cheboli, and Vipin Kumar,“Detecting Anomalies in a Time Series Database”, ACM, Technical Report (2009).

Victoria J. Hodge & Jim Austin,” A Survey of Outlier Detection Methodologies “, Artificial Intelligence Review 22 (2004): 85–126.

Anvardh Nanduri and Lance Sherry, “Anomaly Detection In Aircraft Data Using Recurrent Neural Networks (RNN)”, IEEE Integrated Communications Navigation and Surveillance (ICNS) Conference, 5C2-8(2016):19-21.

Vrushali D. Mane and S.N. Pawar,” Anomaly based IDS using Backpropagation Neural Network”, International Journal of Computer Applications (0975 – 8887) Volume 136 – No.10 (2016):29-34.

Pavel Kachurka and Vladimir Golovko, “Neural Network Approach to Real-Time Network Intrusion Detection and Recognition”, The 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 15-17 (2011): 393-397.

Usman Ahmed and Asif Masood, “Host Based Intrusion Detection Using RBF Neural Networks”, IEEE 2009 International Conference on Emerging Technologies (2009): 48-51.

Tetiana Gladkykh, Taras Hnot and Volodymyr Solskyy, “Fuzzy Logic Inference for Unsupervised Anomaly Detection”, IEEE First International Conference on Data Stream Mining & Processing 23-27 (2016): 42-47.

Hesam Izakian and Witold Pedrycz, “Anomaly Detection in Time Series Data using a Fuzzy C-Means Clustering”, IEEE (2013): 1513-1518.

Linquan Xie, Ying Wang, Liping Chen, and Guangxue Yue,” An Anomaly Detection Method Based on Fuzzy C-means Clustering Algorithm”, Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10), Academy Publisher, 2-4 (2010): 089-092.

Saeed Aghabozorgi and The Ying Wah, “Effective Clustering of Time-Series Data Using FCM”, International Journal of Machine Learning and Computing, Vol. 4, No. 2, (2014): 170-176.

Muna Mhammad T. Jawhar and Monica Mehrotra, “Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network”, International Journal of Computer Science and Security, Volume 4, Issue 3(2010): 285-294.

Sampada Chavan, Khusbu Shah, Neha Dave and Sanghamitra Mukherjee, Ajith Abraham and Sugata Sanyal,” Adaptive Neuro-Fuzzy Intrusion Detection Systems”, IEEE Computer Society Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04) (2004).

Gang Wang, Jinxing Hao, Jian Ma, and Lihua Huang,” A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering”, Elsevier Expert Systems with Applications 37 (2010): 6225–6232.

Dahlia Asyiqin Ahmad Zainaddin and Zurina Mohd Hanapi,” Hybrid of Fuzzy Clustering Neural Network over Nsl Dataset for Intrusion Detection System”, Journal of Computer Science, Volume 9, No. 3 (2013): 391-403.

Prof. D.P. Gaikwad, Sonali Jagtap, Kunal Thakare and Vaishali Budhawant, “Anomaly Based Intrusion Detection System Using Artificial Neural Network and Fuzzy Clustering”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 9 (2012): 1-6.

Swain Sunita, Badajena J Chandrakanta and Rout Chinmayee, “A Hybrid Approach of Intrusion Detection using ANN and FCM”, European Journal of Advances in Engineering and Technology, 3(2), (2016): 6-14.

Bhavana Jain and Vaishali Kolhe, “Hybrid Approach for Classification using Multilevel Fuzzy Min-Max Neural Network”, International Journal of Innovative Research in Computer and Communication Engineering ,Volume 4, Issue 5 (2016): 8636-8640.

Tejwant Singh, Mr. Manish Mahajan, “Performance Comparison of Fuzzy C Means with Respect to Other Clustering Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014: PP. 89-93.

“Feedforward neural networks 1. What is a feedforward neural network?”,accessed on 3 March 2017http://www.fon.hum.uva.nl/praat/manual/Feedforward_neural_networks_1__What_is_a_feedforward_ne.html.

Accessed on 3rd March, 2017 http://cs.stanford.edu/people/eroberts/courses/soco/projects/neuralnetworks/Architecture/feedforward.html

Loren Shure, ”MATLAB R2014b Graphics”, accessed on 6th March, 2017 https://blogs.mathworks.com/loren/2014/10/03/matlab-r2014b-graphics-part-1-features-of-the-new-graphics-system/

I. Levin, “KDD-99 classifier learning contest LLSoft's results overview”, ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, (2000): 67-75.

S.J. Stolfo, A.L. Prodromidis, S. Tselepis, W. Lee, D.W. Fan, and P.K. Chan, “In KDD JAM: Java Agents for Meta-Learning over Distributed Databases”, vol. 97, (1997):74-81.

P.G. Jeya, M. Ravichandran, and C. S. Ravichandran, “Efficient Classifier for R 2 L and U 2 R Attacks” , International Journal of Computer Applications, vol. 45, no. 21, (2012).

S.Paliwal and Ravindra Gupta, “Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm”, International Journal of Computer Applications, vol. 60, no. 19, (2012).

L.Sunitha, M. BalRaju, J. Sasikiran, and E.V. Ramana. "Automatic outlier identification in data mining using IQR in real-time data." International Journal of Advanced Research in Computer and Communication Engineering, vol.3, no.6(2014):7255-7257.




DOI: https://doi.org/10.23956/ijarcsse.v8i7.819

Refbacks

  • There are currently no refbacks.




© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.