An Enhanced Apriori Algorithm for Modernized Intrusion Detection in Data

Authors

  • Bhukya Krishna
  • Dr Geetanjali Amarawat

DOI:

https://doi.org/10.14419/ijet.v7i3.27.19266

Keywords:

Intrusion Recognition Framework, Information Mining, Security, Interruptions, Apriori Calculation

Abstract

Correspondence frameworks are fundamental and it will make different essential issues today. These days, we consider that the firewalls are the basic line of block yet that approachs can't meet the specific necessities of anticipated that framework would accomplish security. A broad piece of the examination has been done here at any rate we are slacking to accomplish security needs. Effectively different models, for example, ADAM, DHP, LERAD and ENTROPHY are proposed to choose security issues yet we require a gainful model to see new sorts of different impedances inside the whole structure. In this paper, we proposed to outline a modernized interruption conspicuous evidence structure which contain two systems, for example, idiosyncrasy and mistreat affirmation. Both are intertwined what's more used to recognize novel ambushes. Our structure proposed to find transient instance of aggressor sharpens, which is profiled utilizing an estimation EAA (Enhanced Apriori Computation). This is examined assorted streets with respect to an unmistakable interface to show the demonstrations of strikes sensibly.

 

References

[1] Ye Changguo , “The Research on the Application of Association Rules Mining Algorithm in Network Intrusion Detection†Transactions on Software Engineering, IEEE Communication Magazine.

[2] Aly Ei-Semary, Janica Edmonds, Jesus Gonzalez-Pino, Mauricio Papa, “Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detectionâ€, in the Proceedings of Workshop on Information Assurance United States Military Academy 2006, IEEE Communication Magazine, West Point, Y,DOI:10.1109/IAW.2006/652083.

[3] Amir Azimi, Alasti, Ahrabi, Ahmad Habibizad Navin, Hadi Bahrbegi, “A New System for Clustering & Classification of Intrusion Detection System Alerts Using SOMâ€, International Journal of Computer Science & Security, Vol: 4, Issue: 6, pp-589-597, 2011.

[4] [4] Anderson.J.P, “Computer Security Threat Monitoring & Surveilanceâ€, Technical Report, James P Anderson co., Fort Washington, Pennsylvania, 1980.

[5] Denning .D.E, â€An Intrusion Detection Modelâ€, Transactions on Software Engineering, IEEE Communication Magazine, 1987,SE-13, PP-222-232,DOI:10.1109/TSE.1987.232894.

[6] Dewan Md, Farid, Mohammed Zahidur Rahman, “Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithmâ€, Journal of Computers, Vol 5, pp-23-31, Jan 2010, DOI:10.4.304/jcp 5.1.

[7] Jake Ryan, Meng - Jang Lin, Risto Miikkulainen, â€Intrusion Detection With Neural Networksâ€, Advances in Neural Information Processing System 10, Cambridge, MA:MIT Press,1998,DOI:10.1.1.31.3570.

[8] Jin-Ling Zhao, Jiu-fen Zhao ,Jian-Jun Li, “Intrusion Detection Based on Clustering Genetic Algorithmâ€, in Proceedings of International Conference on Machine Learning & Cybernetics (ICML),2005,IEEECommunicationMagazine,ISBN:0-7803-9091-DOI:10.1109/ICML.2005.1527621.

[9] Norouzian.M.R, Merati.S, “Classifying Attacks in a Network Intrusion Detection System Based on Artificial Neural Networksâ€, in the Proceedings of 13th International Conference on Advanced Communication Technology(ICACT), 2011,ISBN:978-1-4244-8830-8,pp-868-873.

[10] Oswais.S, Snasel.V, Kromer.P, Abraham. A, “Survey: Using Genetic Algorithm Approach in Intrusion Detection Systems Techniquesâ€, in the Proceedings of 7th International Conference on Computer Information & Industrial Management Applications (CISIM), 2008, International Journal of Computer Applications (0975 – 8887) Volume 35– No.8, December 2011 56 IEEE Communication Magazine,pp-300-307,ISBN:978-0-7695-318-7,DOI:10.1109/CISM.2008-49.

View Full Article: