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A Survey of Pattern Matching Algorithem in Intrusion Detection System Tehran, Iran


  • Iran Telecom Research Center, Tehran, Iran, Islamic Republic of
  • Department of Computer Engineering University of Tehran, Kish International Campus, Tehran, Iran, Islamic Republic of


The usual approach of Intrusion Detection System (IDS) systematic is according to model compatibility which determines the vandalism happening on the network using particular models and orders. To perform this algorithm, casual manner of the network are evaluated by modeling and in the next step it utilized as a draft model for specifying unusual manner. This paper, wants to determine and select the most efficient procedure for this performance by investigation, application and also gathering all kinds of model compatibility technique so that the most proper result is achieved over compatibility known attacks with original models. In this study, to gather all procedures associated with the subject, we surveyed the expressing of model adoption performance from various views. The other context of this study is to evaluate the items for setting the procedures whereas the algorithms are categorized according to significant items which has more influence on the operating of model adoption performance.


Intrusion Detection System, Network, Model Compatibility, Search Procedure.

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  • Davis J, Andrew J. South Australia, Information Security Institute: Data preprocessing for anomaly based network intrusion detection: A review. Computers & Security. 2011 Sep; 30(6-7):353-75.
  • Karthiga P. Optimization of Model Compatibility Algorithm for Network Intrusion Detection System. International Journal of Advanced Research in Computer Science & Technology. 2014 Jan-Mar; 2(1):1-5.
  • Ghorbani A, Tavallaee M, Wei L. Newyork, Springer Science Business Media, LLC: Network Intrusion Detection and Prevention, Concepts and Techniques. 2010.
  • Li S. Jilin University: The research of fast model compatibility algorithm, Based on Snort system (Master Thesis). In Chinese. 2009.
  • Amir H. Tehran, Iran: Amirkabir University of Technology: Auditing Intrusion Detection System using Mobile Agents, (Master thesis). 2003.
  • Erin K. String Compatibility Algorithm, a scalable instrusion detection engine. IEEE Network. 2006 Sep; 12:1-5.
  • Gee A. Christchurch, New Zealand: Department of Computer Science and Software Engineering University of Canterbury: Research into GPU accelerated model compatibility for applications in computer security, (Master Thesis). 2009.
  • Alqadi AA, Musbah A, Ibrahiem M. Multiple Skip Multiple Model Compatibility Algorithm (MSMPMA). IAENG International Journal of Computer Science. 2007 May; 34(2):1-7.
  • Diwate B, Alaspurkar J. Study of Different Algorithms for Model Compatibility. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Mar; 3(3):1-6.
  • Sring S. London: Rolling Hash (Rabin-Karp Algorithm) Intro to Algorithms Recitation. 2011.
  • Pulle R, Vishwas N. Virtual Animation Studio using Inductive Logic Programming and a Searching Algorithm based on Rabin-Karp for Efficient Data Retrieval: 2nd National Conference on Information and Communication Technology (NCICT). 2011; p. 89-93.
  • Mumbai P. Algorithms in Bioinformatics Pro, 19th Ann. IEEE Symp. Field Programmable Custom Computing Machines (FCCM ‘04). 2012 Aug; p. 1-7.
  • Cho Y, Navab S, Mangione-Smith W. String Mathing (Knuth-Morris-Pratt Algorithm). Proc. 21th ACM/SIGDA Int’l Conf. Field-Programmable Logic and Applications (FPL ‘02). 2011 Sep; p. 452-61.
  • Yang W. On the shift-table in Boyer-Moore’s String Compatibility Algorithm. International Journal of Digital Content Technology and its Applications. 2010 May; 3:10-20.
  • Sun K, Yanggon K. A Fast Multiple String-Model Compatibility Algorithm, Encoding function can be dynamically dened w.r.t the set of characters that appears in the input models: adaptive string model compatibility. 2010.
  • Abu-Alhaj M, Halaiyqah M, Muhannad A, Manasrah M. An innovative platform to improve the performance of exactstring compatibility algorithms. (IJCSIS) International Journal of Computer Science and Information Security. 2010 Jun; 7(1):280-83.
  • Wamelena B, Lib Z, Iyengar S. A fast expected time algorithm for the 2-D point modelcompatibility problem. Model Recognition. 2004 Aug; 37(8):1699-1711. Available from:
  • Burcsi P, Ferdinando C, Gabriele F, Zsuzsanna L. Hungary: E-otv-os Lor_and University: Algorithms for Jumbled Model Compatibility in Strings. 2011.
  • Crous C. Dictionary Compatibility Automata The Aho-Corasick Algorithm, BScHons. 2006.
  • Cheng-Hung L, Tsai S, Liu C, Chang S, Shyu J. London: IEEE globalcom: Accelerating String Compatibility Using Multi-threaded Algorithm on GPU. 2010.
  • Kennedy A. Dublin City University, School of Electronic Engineering: Energy Efficient Hardware Accelerators for Packet Classification and String Compatibility, Degree of Doctor of Philosophy thesis. 2010.
  • Taylor E. Applied Research Laboratory Department of Computer Science and Engineering, Washington University in Saint Louis: Survey & Taxonomy of Packet Classification Techniques. 2004.
  • Zhang B, Eugene T. Department of Computer Science Rice University: On Constructing Efficient Shared Decision Trees for Multiple Packet Filters. 2010.
  • Crochemore M. UK, Oxford University Press: Off-line serial exact string searching, in Model Compatibility Algorithms. 1997; p. 1-53.
  • Antonatos S, Polychronakis M, Akritidis P, Anagnostakis KG, Markatos EP. Hellas: Institute of Computer Science Foundation for Research and Technology: Piranha: fast and memory-efficient model compatibility for intrusion detection. 2010.
  • Fanglu G, Chiueh T. NY: Computer Science Department Stony Brook University: Traffic Analysis: From Stateful Firewall to Network Intrusion Detection System. 2011; p. 1-24.
  • Markatos P, Spyros A, Polychronakis M, Kostas G. Institute of Computer Science (ICS), Foundation for Research & Technology – Hellas (FORTH): Exclusion-based Signature Compatibility for Intrusion Detection. 2002.
  • Watson W. Africa: Pretoria University: A new family of Commentz-walter-style multiple-keyword model compatibility algorithm. 1979.
  • Carriero N, Eric F. Institute of Computer Science, Yale University: Adaptive parallelism and piranha. 1994; p. 1-19.
  • Roman A. Poland: Institute of Computer Science, Jagiellonian University: Experiments on Synchronizing Automata. 2010; 19:1-17.
  • Hartlieb B. Functional Dependencies in Fuzzy Databases. In: 21st Computer Science Seminar. 2010.
  • Rajendran R. Hybrid Intrusion Detection Algorithm for Private Cloud. Indian Journal of Science and Technology. 2015 Jun; 8(7):256-63.
  • Zhengqiang K. An improved multiple models compatibility algorithm for intrusion detection. II Proc. of International Conference on Computer Science and Information Technology. IEEE, 2010 Oct; p. 124-27.
  • Seifi S, Gharaei H, Neda B. HC-QWM Algorithm for Model Compatibility in Intrusion Detection System. 19th National CSI Computer Conference (CSICC Tehran, Iran). 2014.
  • Prasad S, Srinath V, Basha M. Intrusion Detection Systems, Tools and Techniques – An Overview. Indian Journal of Science and Technology. 2015 Jul; 8(19):125-30.
  • Zhang B, Eugene TS. Department of Computer Science Rice University: On Constructing Efficient Shared Decision Trees for Multiple Packet Filters. 2010 Mar; p. 2910-18.


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