___

• 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