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Intruder Attack Detection In Data Network Organization Using Data Mining Techniques

Renu Dewli1 , Anubhooti Papola2

  1. Computer Science and Engineering, Faculty of Technology, Uttarakhand Technical University, Dehradun, India.
  2. Computer Science and Engineering, Faculty of Technology, Uttarakhand Technical University, Dehradun, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 544-549, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.544549

Online published on Apr 30, 2018

Copyright © Renu Dewli, Anubhooti Papola . 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.

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IEEE Style Citation: Renu Dewli, Anubhooti Papola, “Intruder Attack Detection In Data Network Organization Using Data Mining Techniques,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.544-549, 2018.

MLA Style Citation: Renu Dewli, Anubhooti Papola "Intruder Attack Detection In Data Network Organization Using Data Mining Techniques." International Journal of Computer Sciences and Engineering 6.4 (2018): 544-549.

APA Style Citation: Renu Dewli, Anubhooti Papola, (2018). Intruder Attack Detection In Data Network Organization Using Data Mining Techniques. International Journal of Computer Sciences and Engineering, 6(4), 544-549.

BibTex Style Citation:
@article{Dewli_2018,
author = {Renu Dewli, Anubhooti Papola},
title = {Intruder Attack Detection In Data Network Organization Using Data Mining Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {544-549},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1898},
doi = {https://doi.org/10.26438/ijcse/v6i4.544549}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.544549}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1898
TI - Intruder Attack Detection In Data Network Organization Using Data Mining Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Renu Dewli, Anubhooti Papola
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 544-549
IS - 4
VL - 6
SN - 2347-2693
ER -

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Abstract

Networked data contain interconnected entities for which inferences are to be made. For example, web pages are interconnected by hyperlinks, research papers are associated by references, phone accounts are linked by calls, conceivable terrorists are linked by communications. Networks have turned out to be ubiquitous. Correspondence networks, financial transaction networks, networks portraying physical systems, and social networks are all ending up noticeably progressively important in our everyday life. Regularly, we are interested in models of how nodes in the system influence each other (for example, who taints whom in an epidemiological system), models for predicting an attribute of intrigue in light of observed attributes of objects in the system. The technique of SVM is applied which will classify the data into malicious and non-malicious. To increase the accuracy of classification technique Knn classier is applied which increase accuracy, execution time.

Key-Words / Index Term

Data network, attacks, data mining,, IDS/IPS machine learning

References

[1] G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: The state of the art,” International journal of forecasting, Vol.14(1), pp. 35-62. 1998


[2] L. M. Manevitz, and M. Yousef, “One-class SVMs for document classification,” Journal of machine Learning research, Vol. 2, pp. 139- 154, 2002

[3] B. Schölkopf, and A. J. Smola, “Learning with kernels: support vector machines, regularization, optimization, and beyond,” MIT press, USA, 2002

[4] C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and N. Kumar, “Supportvector- based fuzzy neural network for pattern classification,” IEEE Trans Fuzzy Systems, Vol.14(1), pp. 31-41, 2006

[5] MF. Amin and K. Murase, “Single-layered complex-valued neural network for real-valued classification problems,” Neurocomputing 72, January 2009, pp. 945-955

[6] Doris Hooi-Ten Wong & Selvakumar Manickam, “Intelligent Expertise Classification Approach: An Innovative Artificial Intelligence Approach To Accelerate Network Data Visualization”, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE)

[7] C. S. Dangare, and S. S. Apte, “Improved study of heart disease prediction system using data mining classification techniques,” International Journal of Computer Applications, Vol. 47(10), pp. 44-48, 2012

[8] HY. Huang, YJ. Lin, YS. Chen and HY. Lu, “Imbalanced data classification using random subspace method and SMOTE,” 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) , November 2012, pp.817-820

[9] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern classification”, John Wiley & Sons, Canada, 2012

[10] AR. Hafiz, AA. Yarub, MF. Amin, and K. Murase, “Classification of Skeletal Wireframe Representation of Hand Gesture using Complex- Valued Neural Network,” Neural Processing Letters 42(3), December 2015, pp.649-664

[11] Pedro Amaral, et.al, “Machine Learning in Software Defined Networks: Data Collection and Traffic Classification”, 2016

[12] Pedro Amaral, Joao Dinis, Paulo Pinto, Luis Bernardo, Joao Tavares, Henrique S. Mamede, “Machine Learning in Software Defined Networks: Data Collection and Traffic Classification”, 2016, IEEE 24th International Conference on Network Protocols (ICNP), Workshop on Machine Learning in Computer Networks (NetworkML 2016)

[13] Zhizhong Kang, Juntao Yang, and Ruofei Zhong, “A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR Data With Optical Images”, 2016, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

[14] Sahan L. Maldeniya, Ajantha S. Atukorale, Wathsala W. Vithanage, “Network Data Classification Using Graph Partition”, 2013, IEEE.


[15] Nikola K. Kasabov, Maryam Gholami Doborjeh, and Zohreh Gholami Doborjeh, “Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks”, 2016, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

[16] Kentarou Matsuda, Kazuyuki Murase, “Single-layered Complex-valued Neural Network with SMOTE for Imbalanced Data Classification”, 2016, Joint 8th International Conference on Soft Computing and Intelligent Systems

[17] Parisa Naraei, Abdolreza Abhari, Alireza Sadeghian, “Application of Multilayer Perceptron Neural Networks and Support Vector Machines in Classification of Healthcare Data”, FTC 2016 - Future Technologies Conference

[18] Joseph D. Prusa, Taghi M. Khoshgoftaar, “Designing a Better Data Representation for Deep Neural Networks and Text Classification”, 2016 IEEE 17th International Conference on Information Reuse and Integration

[19] Lorenzo A. Rossi, Bhaskar Krishnamachari and C.-C. Jay Kuo, “Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data”, 2016, International Conference on Distributed Computing in Sensor Systems

[20] Justin Salamon and Juan Pablo Bello, “Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification”, 2016, IEEE

[21] Bambang Sugiarto, Rika Sustika, “Data Classification for Air Quality on Wireless Sensor Network Monitoring System Using Decision Tree Algorithm”, 2016, 2nd International Conference on Science and Technology-Computer (ICST)

[22] Balasaheb Tarle, Sudarson Jena, “Improved Artificial Neural Network For Dimension Reduction In Medical Data Classification”, 2016, IEEE

[23] Ran Zhang, Lifeng Wu, Xiaohui Fu and Beibei Yao, “Classification of Bearing Data Based on Deep Belief Networks”, 2016, IEEE

[24] Yinfeng Zhao, Lei Li, “Link Prediction-based Multi-label Classification on Networked Data”, 2016, IEEE First International Conference on Data Science in Cyberspace

[25] Saptarshi Rudra, Soham Mitra, Soumyajit Das, Abhisek Roy, Shibasis Guha, “Gender Classification System from Offline Survey Data Using Neural Networks”, 2016, IEEE

[26] Joao Roberto Bertini Junior, Maria do Carmo Nicoletti, “Functionally Expanded Streaming Data as Input to Classification Processes Using Ensembles of Constructive Neural Networks”, 2016, IEEE

[27] Arun Manicka Raja M., Swamynathan S., “ENSEMBLE LEARNING FOR NETWORK DATA STREAM CLASSIFICATION USING SIMILARITY AND ONLINE GENETIC ALGORITHM CLASSIFIERS”, 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI)