A Survey on the State of Art Approaches Used in Intrusion Detection System

Authors(3) :-Satish Kumar, Dr. Sunanda, Dr. Sakshi Arora

The security has always been the prime issue for a user as well as for the network system. Intrusion detection is being used as security other than the first line of security like firewall in which malicious packets are prevented from being penetration to the target. Within the development of the technologies and system resources, there have always been intrusion detection systems which are capable in detection of malicious attack in an efficient manner with less false positive instances. This paper reveals current scenarios of used technologies for the purpose of detection of intrusions.

Authors and Affiliations

Satish Kumar
Research Scholar, Department of CSE, SMVD University, Katra, Jammu and Kashmir, India
Dr. Sunanda
Assistant Professor, Department of CSE, SMVD University, Katra, Jammu and Kashmir, India
Dr. Sakshi Arora

IDS, Anomaly, Malicious Attacks, Detection Rate, False Positive Intrusion.

  1. Richard Zuech, Taghi M. Khoshgoftaar and Randall Wald: "Intrusion detection and Big Heterogeneous Data: A Survey" in Journal of Big Data (2015), DOI 10.1186/s40537-015-0013-4, Springer Open Journal.
  2. Chin-Tser Huang, Rocky K. C. Chang, and Polly Huang: "Signal Processing Applications in Network Intrusion Detection Systems"; Hindawi Publishing Corporation, EURASIP Journal on Advances in Signal Processing, Volume 2009, Article ID 527689, DOI: 10.1155/2009/527689
  3. Praveen Lalwani, Sagnik Das: "Bacterial Foraging Optimization Algorithm for CH selection and Routing in Wireless Sensor Networks"; 3rd International Conference on Recent Advances in Information Technology, RAIT- 2016
  4. Audrey A. Gendreau, Michael: "Survey of Intrusion Detection Systems towards an End to End Secure Internet of Things"; 4th International Conference on Future Internet of Things and Cloud, IEEE, 2016
  5. Shadi Aljawarneha, Monther Aldwairi, Muneer Bani Yasse: "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model"; Journal of Computational Science, Available online 22 March 2017, Elsevier2017. Page 1- 9
  6. Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung: "Intrusion Detection Using Neural Networks and Support Vector Machines"; Proceedings of the International Joint Conference 2002 - IJCNN'02 on Neural Networks, 2002, IEEE 2002, Pages 1702- 1707
  7. Sannasi Ganapathy, Kanagasabai Kulothungan, Sannasy Muthurajkumar, Muthusamy Vijayalakshmi, Palanichamy Yogesh & Arputharaj Kannan: "Intelligent feature selection and classification techniques for intrusion detection in networks: A survey"; EURASIP Journal on Wireless Communications and Networking (A Springer Open journal), 2013, Volume 2013, Issue 01, Artical 271, Page 01- 16.
  8. Mohammad Sazzadul Hoque, Md. Abdul Mukit and Md. Abu Naser Bikas: "An Implementation of Intrusion Detection System Using Genetic Algorithm"; International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012, Pages 109- 120
  9. B.A. Fessi, S. Ben Abdallah, M. Hamdi and N. Boudriga: A New Genetic Algorithm Approach for Intrusion Response System in Computer Networks"; Symposium on Computers and Communications, 5- 8 July 2009, IEEE Xplore 2009, Pages 342- 347, IEEE, 2009.
  10. S. Devaraju and Dr. S. Ramakrishnan: "Performance Analysis Of Intrusion Detection System Using Various Neural Network Classifiers"; IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011, MIT, Anna University, Chennai, Tamil Nadu, India. June 3-5, 2011, IEEE 2011, Pages 1033-1038.
  11. Mohammed A. Ambusaidi, Xiangjian He, Priyadarsi Nanda and Zhiyuan Tan: "Building An Intrusion Detection System Using A Filter-Based Feature Selection Algorithm" IEEE Transactions on Computers, Oct. 1 2016, Volume 65, Issue 10, Pages 2986 – 2998.
  12. Xu Yang, Zhao Hui: "Improving the Particle Swarm Algorithm and Optimizing the Network Intrusion Detection of Neural Network"; Sixth International Conference on Intelligent Systems Design and Engineering Applications, 2015, Date of Conference: 18-19 Aug. 2015, Date Added to IEEE Xplore: 02 May 2016, Pages 452- 455.
  13. A. Gupta, O. J. Pandey, M. Shukla, A. Dadhich, S. Mathur, and A. Ingle: "Computational Intelligence Based Intrusion Detection Systems for Wireless Communication and Pervasive Computing Networks": IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Date of Conference: 26-28 Dec. 2013, Enathi, India, pages 1-7. IEEE, 2013, Pages: 1 - 7
  14. Fatemeh Kavousi and Behzad Akbari: "Automatic Learning of Attack Behaviour Patterns Using Bayesian Networks"; 6th International Symposium on Telecommunications 2012- (IST'2012), Date of Conference: 6-8 Nov. 2012, Date Added to IEEE Xplore: 21 March 2013, Pages 999-1004.
  15. Richard Zuech, Taghi M. Khoshgoftaar and Randall Wald: "Intrusion detection and Big Heterogeneous Data: A Survey"; Journal of Big Data (2015), Journal of Big Data (2015), Volume 2, Issue 1, Article 3, December 2015, Page 1- 41.
  16. Chun Guo, Yuan Ping, Nian Liu, Shou-Shan Luo: "A Two-Level Hybrid Approach For Intrusion Detection"; Neuro computing 214 (2016), Elsevier, 2016 page 391–40
  17. Chi-Ho Tsang, Sam Kwong and HanliWang: "Genetic-Fuzzy Rule Mining Approach And Evaluation Of Feature Selection Techniques For Anomaly Intrusion Detection"; Pattern Recognition Society, Elsevier, 2007
  18. Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri: "Intrusion detection model using fusion of chi-square feature selection and multi class SVM"; Journal of King Saud University – Computer and Information Sciences (2016), Received 7 July 2015; revised 4 October 2015; accepted 3 December 2015.
  19. E. Biermann, E. Cloete, L. M. Venter: "A comparison of Intrusion Detection systems"; Computers & Security, Elsevier Science Ltd, 20 (2001) page 676-683.
  20. TIAN Xin- Guang, GAO Li-zhi, SUN Chun- Lai, DUAN Mi-yi, ZHANG Er-yang: "A Method for Anomaly Detection of User Behaviours Based on Machine Learning"; The Journal Of China Universities of Posts And Telecommunications, Vol. 13, No. 2, Jun. 2006.
  21. Enamul Kabir, Jiankun Hu, Hua Wang, Guangping Zhuod: "A novel statistical technique for intrusion detection systems"; Future Generation Computer Systems, Elsevier, 2017
  22. Roshni Dubey, Pradeep Nandan Pathak: "KNN based Classifier Systems for Intrusion Detection"; International Journal of Advanced Computer Technology (IJACT), Volume-2 Issue-4: Published On August 25, 2013.
  23. Ismail Butun, Salvatore D. Morgera, and Ravi Sankar: "A Survey of Intrusion Detection Systems in Wireless Sensor Networks"; IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION, IEEE COMMUNICATIONS SURVEYS & TUTORIALS, 2013.
  24. P. García-Teodoro, J. Díaz- Verdejo, G. Maciá-Fernández, E.Vázquez: "Anomaly-based network intrusion detection: Techniques, systems and challenges"; Computer & Security, Elsevier, Volume 28, Issues 1–2, February–March 2009, Pages 18-28.
  25. V. Jyothsna, V.V. Rama Prasad: "FCAAIS: Anomaly based network intrusion detection through feature correlation analysis and association impact scale"; The Korean Institute of Communications Information Sciences (KICS), ICT Express, Elsevier (2016) Volume 2, Issue 3, September 2016, Pages 103-116.
  26. Fangjun Kuanga, Weihong Xua, Siyang Zhang: A novel hybrid KPCA and SVM with GA model for intrusion detection; Applied Soft Computing, Elsevier, Volume 18, May 2014, Pages 178-184.
  27. Avita Katal, Mohammad Wazid, R. H. Goudar D. P. Singh: "A Cluster Based Detection and Prevention Mechanism against Novel Datagram Chunk Dropping Attack in MANET Multimedia Transmission"; Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), IEEE 2013, Pages 479- 484
  28. Hamid Bostani, Mansour Sheikhan: "Modification of supervised OPF based intrusion detection systems using unsupervised learning and social network concept"; Pattern Recognition, Elsevier, Volume 62, February 2017, Pages 56–72.
  29. Salma Elhag, Alberto Fernández, Abdullah Bawakid, Saleh Alshomrani: "On the combination of genetic fuzzy systems and pair wise learning for improving detection rates on Intrusion Detection Systems"; Expert Systems with Applications, Elsevier, Volume 42, Issue 1, January 2015, Pages 193-202.
  30. Wenying Feng, Qinglei Zhang, Gongzhu Hu, Jimmy Xiangji Huang: "Mining network data for intrusion detection through combining SVMs with ant colony networks"; Future Generation Computer Systems, Elsevier, Volume 37, July 2014, Pages 127-140.
  31. Seyed Mojtaba Hosseini Bamakan, Huadong Wang, Tian Yingjie, YongShi: "An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization"; Neuro computing, Elsevier, Volume 199, 26 July 2016, Pages 90-102.
  32. Sejal K. Patel, Umang H. Mehta, Urmi M. Patel, Dhruv H.Bhagat, Pratik Nayak and Ankita D. Patel: "A Technical Review on Intrusion Detection System"; International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 6 No. 01 Jan 2015, Pages 17- 22
  33. Mohammed Anbar, Rosni Abdullah, Iznan H. Hasbullah, Yung-Wey Chong and Omar E. Elejla: "Comparative performance analysis of classification algorithms for intrusion detection system"; 14th Annual Conference on Privacy, Security and Trust (PST), Auckland, New Zealand, 12-14 Dec. 2016, Date Added to IEEE Xplore: 24 April 2017.

Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 165-176
Manuscript Number : CSEIT174421
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Satish Kumar, Dr. Sunanda, Dr. Sakshi Arora, "A Survey on the State of Art Approaches Used in Intrusion Detection System", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.165-176, September-2017.
Journal URL : http://ijsrcseit.com/CSEIT174421

Article Preview

Follow Us

Contact Us