Intrusion Detection System with Grey Wolf Optimizer (GWO)

Abstract

Intrusion detection system (IDS) has started becoming a part of every system with a presence of the growing security breaches in the world. Therefore, intrusion-detection systems have the task of monitoring the usage of such systems to detect apparition of insecure states. One of the main challenges has been to build Secure application. Researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments.

In this paper, we present a Grey wolf optimizer (GWO) approach with an improved of the intrusion detection system, this approach used for classifying data and to efficiently detect various of intrusions.

Keywords

• IDS,HIDS,NIDS,Attack,Behavior,Scenario

References

1. Sabahi, F., & Movaghar, A.: Intrusion Detection: A Survey . The Third Interna- tional Conference on Systems and Networks Communications,IEEE computer soci- ety,(2008).
2. Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detection systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomunica- tion,(2000).
3. Ghosh, K., Aaron, S., & Michael, S.: Learning Program Behavior Proles for Intru- sion Detection. Proceedings of the Workshop on Intrusion Detection and Network Monitoring,(1999).
4. Venkatesan, R., Ganesan, R. & Arul Lawrence Selvakumar, A.: A Survey on Intru- sion Detection using Data Mining Techniques .International Journal of Computers and Distributed SystemsVol. No.2, Issue 1,(2012).
5. Ismail,B., Morgera, D., & Ravi, S. : A Survey of Intrusion Detection Systems in Wireless Sensor Networks. IEEE communications surveys tutorials, vol. 16, no. 1, rst quarter,(2014).
6. James P ANDERSON.: Computer security threat monitoring and surveillance. Rapp. tech. Fort Washington, Pennsylvania,(1980).
7. Dorothy,E.: An intrusion-detection mode. In :IEEE Transactions on software engi- neering2, 222-232,(1987).
8. Ludovic, M., & Cdric,M.: La dtection d'intrusions: bref aperu et derniers dveloppe- ments. In: Mars (1999).

9. Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detec- tion systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomuni- cation,(2000).
10. Jacob, Z., & Ludovic, M.: Les systmes de dtection d'intrusions: principes algorith- miques,( 2002).
11. Ni, G., Ling, G., Quanli, G., & Hai, W.: An Intrusion Detection Model Based on Deep Belief Networks. Second International Conference on Advanced Cloud and Big Data,(2014).
12. Cheng, X. , Png Chin,Y. , & Lim Swee, M.: Design of multiple-level hybrid classier for intrusion detection system using Bayesian clustering and decision trees , Pattern Recognition Letters 29 , 918-924,(2008).
13. Atmaja, S., Sonali, N., Akshaya, R., & Burhan,S. ,Pravin, F.: Survey on Intrusion Detection System using Data Mining Techniques . International Research Journal of Engineering and Technology (IRJET),(2017).
14. Monther, A., Yaser, K., & Mohammad ,A.: Application of articial bee colony for intrusion detection systems. Wiley Online Library (wileyonlinelibrary.com),(2012).
15. Mariem, B., Farah, J.: Intrusion Detection Based on Genetic Fuzzy Classication System . IEEE/ACS 13th International Conference of Computer Systems and Ap- plications (AICCSA),(2016).
16. Surat, S.: Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine. Proceedings of the 2007 IEEE Symposium on Computa- tional Intelligence in Security and Defense Applications,(2007).
17. Burguera, I., Zurutuza, U., & Nadjm, S.: Crowdroid: Behavior-Based Malware Detection System for Android. Chicago, Illinois, USA,(2011).
18. Oguz, M., Buckak, I.: A Behavior Based Intrusion Detection System Using Machine Learning Algorithms. International Journal of Articial Intelligence and Expert Sys- tems (IJAE), Volume (7) : Issue (2),(2016).
19. Zanero, S.: Behavioral Intrusion Detection. Via Ponzio 34/5, 20133 Milano, Italy,(2005).
20. Malek, Z., Trivedi, B.: GUI-Based User Behavior Intrusion Detection. IEEE In- ternational Conference on Power, Control, Signals and Instrumentation Engineer- ing,(2017).
21. HANAN, H;, & all.: A Taxonomy and Survey of Intrusion Detection System De- sign Techniques, Network Threats and Datasets, Association for Computing Ma- chinery,Vol. 1, Article No.1,June (2018).
22. Vosooghifard, M;,& Ebrahimpour, H.: Applying Grey Wolf Optimizerbased deci- sion tree c1assifer for cancer classication on gene expression data. International Conference on Computer and Knowledge Engineering (ICCKE) 2015.
23. Mirjalili, S;, & all.: GREY Wolf Optimizer. Advances in Engineering Software 69 (2014) 4661