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Reseach Article

Efficient FSM Techniques for IDS to Reduce the System Attacks

by Dr.M.Sadiq Ali Khan, Dr.S.M.Aqil Burney
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 11
Year of Publication: 2011
Authors: Dr.M.Sadiq Ali Khan, Dr.S.M.Aqil Burney
10.5120/3703-5121

Dr.M.Sadiq Ali Khan, Dr.S.M.Aqil Burney . Efficient FSM Techniques for IDS to Reduce the System Attacks. International Journal of Computer Applications. 29, 11 ( September 2011), 42-47. DOI=10.5120/3703-5121

@article{ 10.5120/3703-5121,
author = { Dr.M.Sadiq Ali Khan, Dr.S.M.Aqil Burney },
title = { Efficient FSM Techniques for IDS to Reduce the System Attacks },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 11 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number11/3703-5121/ },
doi = { 10.5120/3703-5121 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:35.381977+05:30
%A Dr.M.Sadiq Ali Khan
%A Dr.S.M.Aqil Burney
%T Efficient FSM Techniques for IDS to Reduce the System Attacks
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 11
%P 42-47
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main purpose of this research is to introduce the new techniques of Finite State Machine (FSM), mainly DFA and PDA to filter the error attacks of an Intrusion Detection System. The main purpose of implementing the PDA model in attacks is to minimize the space required which is important issue in network when attacks are push and slow down the network traffic. The data transfer from Intrusion Detection Model in a certain time on random basis, may the system will hang due to huge amount of data but if we will use Time Management Automata we can easily remove such problem.

References
  1. S.M.Aqil Burney and M.Sadiq Ali Khan (2010); “Network Usage Security Policies for Academic Institutions”, International Journal of Computer Applications, October Issue, Published By Foundation of Computer Science.
  2. S.M.Aqil Burney; M.Sadiq Ali Khan and Mr.Jawed Naseem (2010); “Efficient Probabilistic Classification Methods for NIDS”; (IJCSIS) International Journal of Computer Science and Information Security,Vol. 8, No. 8, November 2010
  3. Roschke, S.; Feng Cheng; Meinel, C.(2009), “An Extensible and Virtualization-Compatible IDS Management Architecture”, Fifth International Conference on Information Assurance and Security IAS '09.Volume: 2, DOI 10.1109/IAS.2009.151 Page(s): 130 – 134.
  4. Zaman, S.; Karray, F.(2009), “Lightweight IDS Based on Features Selection and IDS Classification Scheme”, International Conference on Computational Science and Engineering, CSE '09, Volume: 3, DOI: 10.1109/CSE.2009.180, Page(s): 365 - 370.
  5. Orfila, A.; Carbo, J.; Ribagorda, A.(2003), “Fuzzy logic on decision model for IDS”, The 12th IEEE International Conference on Fuzzy Systems, Volume: 2, DOI 10.1109/FUZZ.2003.1206608, Page(s): 1237 - 1242
  6. Murakami, M.; Honda, N. (2009), “Development of an IDS hardware unit for real-time learning applications”, International Conference on Fuzzy Systems, DOI 10.1109/FUZZY.2009.5277072, Page(s): 227 – 233.
  7. Mathew, B.K.; John, S.K.; Pradeep, C.(2008), “New Technique for Fault Detection in Quantum Cellular Automata”, First International Conference on Emerging Trends in Engineering and Technology ICETET ’08, DOI 10.1109/ICETET.2008.186, Page(s): 834 – 837.
  8. Preston, K., Jr.(1990), “ Detection of weak, sub pixel targets using mesh-connected cellular automata”, IEEE Transactions on Aerospace and Electronic Systems, Vol 26 Issue 3, DOI 10.1109/7.106134, Page(s): 548 - 559
  9. Yasami, Y.; Mozaffari, S.P.; Khorsandi, S.(2008), “Stochastic learning automata-based time series analysis for network anomaly detection”, International Conference on Telecommunications ICT 08, DOI 10.1109/ICTEL.2008.4652664.Page(s): 1 – 6.
  10. Zong-Fen Han; Jian-Ping Zou; Hai Jin; Yan-Ping Yang; Jian-Hua Sun(2004), “ Intrusion detection using adaptive time-dependent finite automata”, International Conference on Machine Learning and Cybernetics Volume: 5, DOI 10.1109/ICMLC.2004.1378554 , Page(s): 3040 - 3045 vol.5
  11. Sinaie, S.; Ghanizadeh, A.; Majd, E.M.; Shamsuddin, S.M.(2009), “A Hybrid Edge Detection Method Based on Fuzzy Set Theory and Cellular Learning Automata”, International Conference on Computational Science and Its Applications ICCSA '09, DOI 10.1109/ICCSA.2009.19, Page(s): 208 - 214
  12. Toony, Z.; Jamzad, M.(2010), “A modified saliency detection for content-aware image resizing using cellular automata “,International Conference on Signal and Image Processing (ICSIP), DOI 10.1109/ICSIP.2010.5697464, Page(s): 175 – 179.
Index Terms

Computer Science
Information Sciences

Keywords

Finite State Machine Deterministic Finite Automata Push Down Automata Intrusion Detection System