We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Conceptual Framework for Soft Computing based Intrusion Detection to Reduce False Positive Rate

by Dharmendra G. Bhatti, P. V. Virparia, Bankim Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 13
Year of Publication: 2012
Authors: Dharmendra G. Bhatti, P. V. Virparia, Bankim Patel
10.5120/6320-8667

Dharmendra G. Bhatti, P. V. Virparia, Bankim Patel . Conceptual Framework for Soft Computing based Intrusion Detection to Reduce False Positive Rate. International Journal of Computer Applications. 44, 13 ( April 2012), 1-3. DOI=10.5120/6320-8667

@article{ 10.5120/6320-8667,
author = { Dharmendra G. Bhatti, P. V. Virparia, Bankim Patel },
title = { Conceptual Framework for Soft Computing based Intrusion Detection to Reduce False Positive Rate },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 13 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number13/6320-8667/ },
doi = { 10.5120/6320-8667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:26.450414+05:30
%A Dharmendra G. Bhatti
%A P. V. Virparia
%A Bankim Patel
%T Conceptual Framework for Soft Computing based Intrusion Detection to Reduce False Positive Rate
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 13
%P 1-3
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the popularity and usage of Internet increases security concerns are also become important. Everyone want to be connected to the world through Internet protecting own resources. Intrusion Detection System is one of lucrative area for researchers since long. Numbers of researchers have worked for increasing efficiency of Intrusion Detection Systems. But still many challenges are present in modern Intrusion Detection Systems. One of the major challenges is controlling false positive rate. In this paper we have proposed Soft Computing based Intrusion Detection. We have suggested Genetic Algorithm based solution for Intrusion Detection. In place of standalone Genetic Algorithm we have proposed ensemble soft computing techniques for better results.

References
  1. Amir Azimi Alasti Ahrabi, Ahmad Habibizad Navin, Hadi Bahrbegi, Mir Kamal Mirnia, Mehdi Bahrbegi, Elnaz Safarzadeh, Ali Ebrahimi, A New System for Clustering and Classification of Intrusion Detection System Alerts Using Self-Organizing Maps, International Journal of Computer Science and Security, (IJCSS), Volume (4): Issue (6), 2011
  2. Aung Htike Phyo, Steven Furnell, Emmanuel Ifeachor, A Framework for Monitoring Insider Misuse of IT Applications, Peer-reviewed Proceedings of the ISSA 2004 enabling tomorrow Conference, ISBN 1-86854-522-9, 2004
  3. D. A. Karras, V. Zorkadis, Neural Network Techniques for Improved Intrusion Detection in Communication Systems, Proceedings of the 5th WSES International Conference on Circuits, Systems, Communications and Computers (CSCC 2001) ISBN: 960-8052-33-5, 2001
  4. Damiano Bolzoni, Sandro Etalle, APHRODITE: an Anomaly-based Architecture for False Positive Reduction, Cornell University Library, Subjects: Cryptography and Security (cs. CR), Report number: TR-CTIT-06-13, arXiv:cs/0604026v1 [cs. CR], 2006
  5. Dan Gorton, Extending Intrusion Detection with Alert Correlation and Intrusion Tolerance, Thesis for the degree of licentiate of engineering, Chalmers University of Technology, Goteborg, Sweden, 2003
  6. G. V. S. N. R. V. Prasad, Y. Dhanalakshmi, Dr. V. Vijaya Kumar, Dr I. Ramesh Babu, Modeling An Intrusion Detection System Using Data Mining And Genetic Algorithms Based On Fuzzy Logic, IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 7, July 2008
  7. Huy Anh Nguyen, Deokjai Choi, Application of Data Mining to Network Intrusion Detection: Classifier Selection Model, APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management, ISBN: 978-3-540-88622-8, 2008
  8. Brian Eugene Lavender, Implementation of Genetic Algorithms into a Network Intrusion Detection System (netGA), and Integration into nProbe, M. S. Project, CALIFORNIA STATE UNIVERSITY, SACRAMENTO, 2010
  9. Jing Xiao-Pei, Wang Hou-Xiang, A new Immunity Intrusion Detection Model Based on Genetic Algorithm and Vaccine Mechanism, I. J. Computer Network and Information Security, 2010, 2, 33-39
  10. Mahmoud Jazzar, Aman Jantan, A Novel Soft Computing Inference Engine Model for Intrusion Detection, IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 4, April 2008
  11. Mansour Sheikhan and Zahra Jadidi, Misuse Detection Using Hybrid of Association Rule Mining and Connectionist Modeling, World Applied Sciences Journal 7 (Special Issue of Computer & IT): 31-37, ISSN 1818-4952, 2009
  12. Muna Elsadig, Azween Abdullah, Biological Inspired Intrusion Prevention and Self-healing System for Network Security Based on Danger Theory, International Journal of Video & Image Processing and Network Security Vol: 9 No: 9, 2008
  13. Obbo Aggrey, An Intrusion Detection System For Academic Institutions, Master of Science Thesis, Makerere University, July 2007
  14. P. Kiran Sree, Exploring a Novel Approach for Providing Software Security Using Soft Computing Systems, International Journal of Security and its Applications, Vol. 2, No. 2, April 2008
  15. S. Elahi, A. Shayan, B. Abdi, Designing a Framework for Convergent Information Security Management among Federated Organizations, World Applied Sciences Journal 4 (Supple 2): 21-32, ISSN 1818-4952, 2008
  16. Sandhya Peddabachigaria, Ajith Abrahamb, Crina Grosanc, Johnson Thomasa, Modeling intrusion detection system using hybrid intelligent systems, Journal of Network and Computer Applications 30 (2007) 114–132, 2007
  17. Suhair Hafez Amer, Enhansing Host based Intrusion Detection Systems with Danger Theory of Artificial Immune Systems, Ph. D. Thesis, Auburn University, Alabama, May 2008
  18. Tadeusz Pietraszek, Alert Classification to Reduce False Positive in Intrusion Detection, Dissertation thesis submitted to Institut fur Informatik, Albert-Ludwigs-Universitat Freiburg, Germany, 2006
  19. Tao Wan, Intrudetector: A Software Platform for Testing Network Intrusion Detection Algorithm, Master of Science Thesis, University of Regina, Canada, 2000
  20. Te-Shun Chou, Cyber Security Threats Detection Using Ensemble Architecture, International Journal of Security and Its Applications Vol. 5 No. 2, April, 2011
  21. Thomas A, RAPID: Reputation based approach for improving intrusion detection effectiveness, Information Assurance and Security (IAS), 2010 Sixth International Conference, On page(s): 118 - 124, Print ISBN: 978-1-4244-7407-3, 23-25 Aug. 2010
  22. Wanli Ma, John Campbell, Dat Tran, and Dale Kleeman, A Conceptual Framework for Assessing Password Quality, IJCSNS International Journal of Computer Science and Network Security, VOL. 7 No. 1, January 2007
  23. Witcha Chimphlee, Abdul Hanan Abdullah, Mohd Noor Md Sap, Siriporn Chimphlee, Surat Srinoy, A Rough-Fuzzy Hybrid Algorithm for Computer Intrusion Detection, The Internaltional Arab Journal of Information Technology, Vol. 4, No. 3, July 2007
  24. Zorana Bankovic, Jose M. Moya, Alvaro Araujo, Slobodan Bojanic and Octavio Nieto-Taladriz, A Genetic Algorithm-based Solution for Intrusion Detection, Journal of Information Assurance and Security 4 (2009) 192-199, 2009
Index Terms

Computer Science
Information Sciences

Keywords

Conceptual Framework Intrusion Detection Soft Computing