Research Article
BibTex RIS Cite

Intrusion Detection System with Grey Wolf Optimizer (GWO)

Year 2019, Volume: 2 Issue: 2, 45 - 60, 30.12.2019

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.

References

  • Sabahi, F., & Movaghar, A.: Intrusion Detection: A Survey . The Third Interna- tional Conference on Systems and Networks Communications,IEEE computer soci- ety,(2008).
  • Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detection systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomunica- tion,(2000).
  • 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).
  • 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).
  • 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).
  • James P ANDERSON.: Computer security threat monitoring and surveillance. Rapp. tech. Fort Washington, Pennsylvania,(1980).
  • Dorothy,E.: An intrusion-detection mode. In :IEEE Transactions on software engi- neering2, 222-232,(1987).
  • Ludovic, M., & Cdric,M.: La dtection d'intrusions: bref aperu et derniers dveloppe- ments. In: Mars (1999).
  • Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detec- tion systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomuni- cation,(2000).
  • Jacob, Z., & Ludovic, M.: Les systmes de dtection d'intrusions: principes algorith- miques,( 2002).
  • 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).
  • 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).
  • 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).
  • Monther, A., Yaser, K., & Mohammad ,A.: Application of arti cial bee colony for intrusion detection systems. Wiley Online Library (wileyonlinelibrary.com),(2012).
  • 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).
  • 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).
  • Burguera, I., Zurutuza, U., & Nadjm, S.: Crowdroid: Behavior-Based Malware Detection System for Android. Chicago, Illinois, USA,(2011).
  • 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).
  • Zanero, S.: Behavioral Intrusion Detection. Via Ponzio 34/5, 20133 Milano, Italy,(2005).
  • Malek, Z., Trivedi, B.: GUI-Based User Behavior Intrusion Detection. IEEE In- ternational Conference on Power, Control, Signals and Instrumentation Engineer- ing,(2017).
  • 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).
  • 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.
  • Mirjalili, S;, & all.: GREY Wolf Optimizer. Advances in Engineering Software 69 (2014) 4661
Year 2019, Volume: 2 Issue: 2, 45 - 60, 30.12.2019

Abstract

References

  • Sabahi, F., & Movaghar, A.: Intrusion Detection: A Survey . The Third Interna- tional Conference on Systems and Networks Communications,IEEE computer soci- ety,(2008).
  • Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detection systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomunica- tion,(2000).
  • 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).
  • 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).
  • 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).
  • James P ANDERSON.: Computer security threat monitoring and surveillance. Rapp. tech. Fort Washington, Pennsylvania,(1980).
  • Dorothy,E.: An intrusion-detection mode. In :IEEE Transactions on software engi- neering2, 222-232,(1987).
  • Ludovic, M., & Cdric,M.: La dtection d'intrusions: bref aperu et derniers dveloppe- ments. In: Mars (1999).
  • Herv, D., Marc, D., & Andreas, W.: A revised taxonomy for intrusion detec- tion systems. IBM Research Devision, Zurich Research Laboraty.ANN telecomuni- cation,(2000).
  • Jacob, Z., & Ludovic, M.: Les systmes de dtection d'intrusions: principes algorith- miques,( 2002).
  • 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).
  • 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).
  • 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).
  • Monther, A., Yaser, K., & Mohammad ,A.: Application of arti cial bee colony for intrusion detection systems. Wiley Online Library (wileyonlinelibrary.com),(2012).
  • 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).
  • 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).
  • Burguera, I., Zurutuza, U., & Nadjm, S.: Crowdroid: Behavior-Based Malware Detection System for Android. Chicago, Illinois, USA,(2011).
  • 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).
  • Zanero, S.: Behavioral Intrusion Detection. Via Ponzio 34/5, 20133 Milano, Italy,(2005).
  • Malek, Z., Trivedi, B.: GUI-Based User Behavior Intrusion Detection. IEEE In- ternational Conference on Power, Control, Signals and Instrumentation Engineer- ing,(2017).
  • 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).
  • 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.
  • Mirjalili, S;, & all.: GREY Wolf Optimizer. Advances in Engineering Software 69 (2014) 4661

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Kouidri CHAİMAA

Mebarka YAHLALI

Mohamed Amine BOUDIA

Abdelmalek AMINE This is me

Reda Mohamed HAMOU This is me

Kouidri SİHAM

Publication Date December 30, 2019
Acceptance Date December 30, 2019
Published in Issue Year 2019 Volume: 2 Issue: 2

Cite

APA CHAİMAA, K., YAHLALI, M., BOUDIA, M. A., AMINE, A., et al. (2019). Intrusion Detection System with Grey Wolf Optimizer (GWO). International Journal of Informatics and Applied Mathematics, 2(2), 45-60.
AMA CHAİMAA K, YAHLALI M, BOUDIA MA, AMINE A, HAMOU RM, SİHAM K. Intrusion Detection System with Grey Wolf Optimizer (GWO). IJIAM. December 2019;2(2):45-60.
Chicago CHAİMAA, Kouidri, Mebarka YAHLALI, Mohamed Amine BOUDIA, Abdelmalek AMINE, Reda Mohamed HAMOU, and Kouidri SİHAM. “Intrusion Detection System With Grey Wolf Optimizer (GWO)”. International Journal of Informatics and Applied Mathematics 2, no. 2 (December 2019): 45-60.
EndNote CHAİMAA K, YAHLALI M, BOUDIA MA, AMINE A, HAMOU RM, SİHAM K (December 1, 2019) Intrusion Detection System with Grey Wolf Optimizer (GWO). International Journal of Informatics and Applied Mathematics 2 2 45–60.
IEEE K. CHAİMAA, M. YAHLALI, M. A. BOUDIA, A. AMINE, R. M. HAMOU, and K. SİHAM, “Intrusion Detection System with Grey Wolf Optimizer (GWO)”, IJIAM, vol. 2, no. 2, pp. 45–60, 2019.
ISNAD CHAİMAA, Kouidri et al. “Intrusion Detection System With Grey Wolf Optimizer (GWO)”. International Journal of Informatics and Applied Mathematics 2/2 (December 2019), 45-60.
JAMA CHAİMAA K, YAHLALI M, BOUDIA MA, AMINE A, HAMOU RM, SİHAM K. Intrusion Detection System with Grey Wolf Optimizer (GWO). IJIAM. 2019;2:45–60.
MLA CHAİMAA, Kouidri et al. “Intrusion Detection System With Grey Wolf Optimizer (GWO)”. International Journal of Informatics and Applied Mathematics, vol. 2, no. 2, 2019, pp. 45-60.
Vancouver CHAİMAA K, YAHLALI M, BOUDIA MA, AMINE A, HAMOU RM, SİHAM K. Intrusion Detection System with Grey Wolf Optimizer (GWO). IJIAM. 2019;2(2):45-60.

International Journal of Informatics and Applied Mathematics