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A Literature Survey and Comprehensive Study of Intrusion Detection

by Sravan Kumar Jonnalagadda, Ravi Prakash Reddy I
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 16
Year of Publication: 2013
Authors: Sravan Kumar Jonnalagadda, Ravi Prakash Reddy I
10.5120/14210-2458

Sravan Kumar Jonnalagadda, Ravi Prakash Reddy I . A Literature Survey and Comprehensive Study of Intrusion Detection. International Journal of Computer Applications. 81, 16 ( November 2013), 40-47. DOI=10.5120/14210-2458

@article{ 10.5120/14210-2458,
author = { Sravan Kumar Jonnalagadda, Ravi Prakash Reddy I },
title = { A Literature Survey and Comprehensive Study of Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 16 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number16/14210-2458/ },
doi = { 10.5120/14210-2458 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:15.405600+05:30
%A Sravan Kumar Jonnalagadda
%A Ravi Prakash Reddy I
%T A Literature Survey and Comprehensive Study of Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 16
%P 40-47
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. As the threat becomes a serious matter year by year, intrusion detection technologies are indispensable for network and computer security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. In order to detect the intrusion, various approaches have been developed and proposed over the last decade. In this paper, a detailed survey of intrusion detection based various techniques has been presented. Here, the techniques are classified as follows: i) papers related to Neural network ii) papers related to Support vector machine iii) papers related to K-means classifier iv) papers related to hybrid technique and v) paper related to other detection techniques. For comprehensive analysis, detection rate, time and false alarm rate from various research papers have been taken.

References
  1. Anderson, J. P. " Computer security threat monitoring and surveillance," Technical Report, Fort Washington, PA, USA, 1980.
  2. Endorf. C, Schultz, E. , & Mellander, J. "Intrusion detection and prevention," California: McGraw-Hill, 2004.
  3. Silva, L. D. S. , Santos, A. C. , Mancilha, T. D. , Silva, J. D. , & Montes, A, "Detecting attack signatures in the real network traffic with ANNIDA,"Expert Systems with Applications, vol. 34, no. 4, pp. 2326–2333, 2008.
  4. Heady R. , Luger G. , Maccabe A. , and Servilla M. "The architecture of a Network level intrusion detection system," Technical Report, CS90-20, Dept. of Computer Science, University of New Mexico, Albuquerque, NM 87131,1990.
  5. Denning D. "An Intrusion-Detection Model," IEEE Transactions on Software Engineering, Vol. SE-13, No. 2, pp. 222-232, 1987.
  6. Kumar S. , Spafford E. H. "An Application of Pattern Matching in Intrusion Detection," Technical Report CSD-TR-94-013. Purdue University, 1994.
  7. Ryan J. , Lin M-J. , Miikkulainen R. (1998) "Intrusion Detection with Neural Networks," Advances in Neural Information Processing Systems, Vol. 10, and Cambridge, MA: MIT Press.
  8. Terran lane, Carla E. Brodley, Temporal Sequence Learning and Data Reduction for anomaly Detection, Vol. 2, No. 3, pp. 295- 331,August 1999.
  9. Cannady J. , "Artificial neural networks for misuse detection," Proceedings of the '98 National Information System Security Conference (NISSC'98), Arlington: Virginia Press, pp. 443-456, 1998.
  10. Shon T, Seo J, and Moon J, "SVM approach with a genetic algorithm for network intrusion detection," Proceedings of the 20th International Symposium on Computer and Information Sciences (ISCIS 05), Berlin: Springer Verlag, pp. 224-233, 2005.
  11. Yu Y, and Huang Hao, "An ensemble approach to intrusion detection based on improved multi-objective genetic algorithm," Journal of Software, Vol. 18, No. 6, June 2007, pp. 1369-1378.
  12. J. Luo, and S. M. Bridges, "Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection," International Journal of Intelligent Systems, pp. 687-703, 2000.
  13. Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham," Fuzzy Classification by Evolutionary Algorithms," IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 313 - 318, 2011.
  14. W. K. Lee, and S. J. Stolfo, "A data mining framework for building intrusion detection model," Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA: IEEE Computer Society Press, pp. 120-132, 1999.
  15. Marjan Bahrololum, Elham Salahi, Mahmoud Khaleghi,"An Improved Intrusion Detection Technique based on two Strategies Using Decision Tree and Neural Network," Journal of Convergence Information Technology,Vol. 4, No. 4, December 2009.
  16. M. Bahrololum, E. Salahi and M. Khaleghi, "Anomaly intrusion detection design Using Hybrid of Unsupervised and supervised neural Network," International Journal of Computer Networks & Communications (IJCNC), Vol. 1, No. 2, July 2009.
  17. Latifur Khan, Mamoun Awad, Bhavani Thuraisingham,"A new intrusion detection system using support vector machines and hierarchical clustering," Journal of VLDB Journal, vol. 16, pp. 507-521, 2007.
  18. Iftikhar Ahmad,Azween Abdullah,Abdullah Alghamdi,Muhammad Hussain,"Optimized intrusion detection mechanism using soft computing techniques,"Telecommun System,2011.
  19. S. Ganapathy, P. Yogesh, and A. Kannan," An Intelligent Intrusion Detection System for Mobile Ad-Hoc Networks Using Classification Techniques," Advances in Power Electronics and Instrumentation Engineering, Communications in Computer and Information Science Vol. 148, pp 117-122,2011.
  20. Carlos A. Catania, Facundo Bromberg, Carlos Garc?a Garino,"An Autonomous Labeling approach to Support Vector Machines Algorithms for Network Traffic Anomaly Detection," Preprint submitted to Expert Systems with Applications, Vol. 39, no. 2, pp. 1822-1829, February, 2012.
  21. Yu Guan, Nabil Belacel and Ali A. Ghorbani,"Y-Means: A Clustering Method for Intrusion Detection," Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 1083- 1086, 2003.
  22. K. M. Faraoun and A. Boukelif,"Neural Networks Learning Improvement using the K-Means Clustering Algorithm to Detect Network Intrusions," International Journal of Computational Intelligence, Vol. 3, no. 2, 2005.
  23. Shekhar R. Gaddam, Vir V. Phoha and Kiran S. Balagani,"K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods,"IEEE Transactions On Knowledge And Data Engineering, Vol. 19, No. 3, March 2007.
  25. K. S. Anil Kumar and Dr. V. Nanda Mohan, "Novel Anomaly Intrusion Detection Using Neuro-Fuzzy Inference System, "IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 8, August 2008.
  27. Gang Wang, Jinxing Hao, Jian Ma, Lihua Huang, "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering, "Expert Systems with Applications, 2010.
  28. Mostafa A. Salama, Heba F. Eid, Rabie A. Ramadan, Ashraf Darwish, and Aboul Ella Hassanien,"Hybrid Intelligent Intrusion Detection Scheme," Soft Computing in Industrial Applications Advances in Intelligent and Soft Computing,Vol. 96, pp. 293-303,2011.
  29. Iwan Syarif, Ed Zaluska, Adam Prugel-Bennett, Gary Wills, "Application of bagging, boosting and stacking to intrusion detection, "Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition,pp. 593-602,2012.
  30. Mrutyunjaya Panda, Ajith Abraham, Manas Ranjan Patra. a "A Hybrid Intelligent Approach for Network Intrusion Detection," International Conference on Communication Technology and System Design, Procedia Engineering, vol. 30, pp. 1-9,2012.
  31. Giorgio Giacinto, Fabio Roli, and Luca Didaci, "Fusion of Multiple Classifiers for Intrusion Detection in Computer Networks," Journal of Pattern Recognition Letters, vol. 24, pp. 1795-1803, 2003.
  32. Suseela T. Sarasamma, Qiuming A. Zhu, and Julie Huff "Hierarchical Kohonenen Net for Anomaly Detection in Network Security," IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, Vol. 35, No. 2, April 2005.
  33. Adel Nadjaran Toosi, Mohsen Kahani, "A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers," Computer Communications vol. 30, pp. 2201–2212, 2007.
  34. H. Gunes Kayacik, A. Nur Zincir-Heywood, Malcolm I. Heywood," A Hierarchical SOM based Intrusion Detection System, "Engineering Applications of Artificial Intelligence,Vol. 20,no. 4, pp. 439-451,June 2007.
  35. Yang Li, Li Guo, "An active learning based TCM-KNN algorithm for supervised network intrusion detection," computers & security, vol. 26, pp. 459-467, 2007.
  36. Saroj Kumar Panigrahy, Jyoti Ranjan Mahapatra, Jignyanshu Mohanty and Sanjay Kumar Jena, "Anomaly Detection in Ethernet Networks using Self Organizing Maps," Department of Computer Science,2009.
  37. Dewan Md. Farid, Mohammad Zahidur Rahman, "Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm," Journal of Computers, Vol. 5, No. 1, January 2010.
  38. Muna M. Taher Jawhar and Monica Mehrotra, "Anomaly Intrusion Detection System using Hamming Network Approach," International Journal of Computer Science & Communication,Vol. 1, No. 1, pp. 165-169, January-June 2010.
  39. Mr. Vivek A. Patole,Mr. V. K. Pachghare,Dr. Parag Kulkarni," Self Organizing Maps to Build Intrusion Detection System," International Journal of Computer Applications,pp. 0975-8887, Vol. 1,No. 8,2010.
  40. Hesham Altwaijry, Saeed Algarny," Bayesian based intrusion detection system," Journal of King Saud University, Computer and Information Sciences, 2010.
  41. Kapil Kumar Gupta, Baikunth Nath, and Ramamohanarao Kotagiri,"Layered Approach Using Conditional Random Fields for Intrusion Detection,"IEEE Transactions On Dependable And Secure Computing, Vol. 7, No. 1, January-March 2010.
  42. Todd Vollmer, Jim Alves-Foss, Milos Manic," Autonomous Rule Creation for Intrusion Detection," IEEE Symposium on Computational Intelligence in Cyber Security (CICS), pp. 1-8, 2011.
  43. Kamran Shafi, Hussein A. Abbass, "Evaluation of an Adaptive Genetic-Based Signature Extraction System for Network Intrusion Detection, "Pattern Analysis and Applications, November 2011.
  44. Iwan Syarif, Adam Prugel-Bennett, Gary Wills, "Unsupervised clustering approach for network anomaly detection," Fourth International Conference on Networked Digital Technologies, 24 - 26 Apr 2012.
  45. Sajal Bhatia, Desmond Schmidt, George Mohay,"Ensemble-based DDoS detection and mitigation model, "Proceedings of the Fifth International Conference on Security of Information and Networks,pp. 79-86,2012.
  46. Prasanta Gogoi, Monowar H Bhuyan, D K Bhattacharyya, and J K Kalita,"Packet and Flow Based Network Intrusion Dataset," Contemporary Computing Communications in Computer and Information Science, vol. 306, pp. 322-334, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion detection clustering classifier detection rate false alarm rate

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