ارائه یک سیستم تشخیص نفوذ جدید مبتنی بر ماشین بردار پشتیبان و بهینه‌سازی کلونی زنبور مصنوعی بهبودیافته

نوع مقاله : علمی-پژوهشی

نویسندگان

1 گروه کامپیوتر- واحد نیشابور - دانشگاه آزاد اسلامی

2 گروه کامپیوتر - واحد مشهد - دانشگاه آزاد اسلامی

چکیده

میزان نفوذ در شبکه در حال افزایش است. سیستم تشخیص نفوذ، می‌تواند تا حد زیادی از حملات به شبکه جلوگیری کند. انتخاب ویژگی یک موضوع حیاتی در سیستم‌های تشخیص نفوذ می‌باشد که بر روی صحت و کارایی آن تأثیر بسزایی دارد. در این تحقیق، یک سیستمِ تشخیصِ نفوذ در شبکهِ ترکیبیِ جدید با استفاده از الگوریتم کلونی زنبور مصنوعی بهبودیافته مبتنی بر طبقه‌بند ماشین بردار پشتیبان با روش ارزیابی 10-fold برای انتخاب بهترین ویژگی‌ها پیشنهاد گردیده است. ایده اصلی، از ترکیب معادلات جستجوی بهینه‌سازی ازدحام ذرات و تکاملی تفاضلی در فاز زنبورهای کارگر و ناظر به‌منظور به‌روزرسانی موقعیت زنبورها و به‌کارگیری پرواز لوی در فاز زنبورهای پیشاهنگ، به‌منظور بهبود بهره‌برداری و نرخ همگرایی در الگوریتم کلونی زنبور مصنوعی می‌باشد. روش پیشنهادی مقاومت و پایداری خود را بر روی مجموعه‌داده NSL-KDD نشان داده و به‌طور قابل توجهی توانسته به بهبود عملکرد کلی سیستم تشخیص نفوذ با صحت 98/97 درصد کمک کند.

کلیدواژه‌ها

عنوان مقاله [English]

A Novel Intrusion Detection System Based on Support Vector Machine and Improved Artificial Bee Colony Optimization

نویسندگان [English]

  • T. Feizi 1
  • M. H. Moattar 2
1 Computer Engineering Department, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
2 Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran
چکیده [English]

Intrusion in the network is increasing. Intrusion detection system can greatly prevent network attacks. Feature selection is a critical issue in intrusion detection systems which have a considerable impact on the accuracy and effectiveness of the system. In this study, a new hybrid network intrusion detection system with improved artificial bee colony algorithm using support vector machine classifier is proposed for feature selection. The main idea is utilizing a combination of search equations of particle swarm optimization and Differential Evolution for updating bee’s position of employed and onlooker bees and utilizing levy flight on scout bees phase, to improve exploitation and increase the convergence rate of the standard artificial bee colony algorithm. The robustness and stability of the proposed approach is evaluated on NSL-KDD dataset and showed significant improvement on the overall performance of intrusion detection system with an accuracy of 98.97 percent.

کلیدواژه‌ها [English]

  • Intrusion Detection System
  • Artificial Bee Colony Algorithm
  • Support Vector Machine
  • Evolutionary Optimization
  • Levy Flight

مراجع

[1] H. J. Liao, C. H. R. Lin, Y. C. Lin and K. Y. Tung, “Intrusion detection system: a comprehensive review,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 16-24, 2013.
[2] رحیم به جانی، محمد کلانتری و امیر مسعود افتخاری مقدم، «ارائه چهارچوبی مبتنی بر نظریه بازی‌ها برای جلب مشارکت گره‌ها در فرآیند شناسایی گره‌های مخرب در شبکه‌های حسگر بی‌سیم»، مجله مهندسی برق دانشگاه تبریز،مقالات آماده انتشار، 1396.
[3] A. Eesa, Z. Orman and A. Brifcani, “A new feature selection model based on ID3 and bees algorithm for intrusion detection system,” Turkish Journal of Electrical Engineering  and Computer Sciences, vol. 23, pp. 615-622, 2015.
[4] A .M. Hosseinzadeh and P. Kabiri, “Feature selection for intrusion detection system using ant colony optimization,” International Journal of Network Security, vol. 18, no. 3, pp. 420-432, 2016.
[5] L. Mohammadpour, M. Hussain, A. Aryanfar, V. Maleki Raee and F. Sattar, “Evaluating performance of intrusion detection system using support vector machines: review,” International Journal of Security and Its Applications, vol. 9, no. 9, pp. 225-234, 2015.
[6] P. Amudha, S. Karthik and S. Sivakumari, “A hybrid swarm intelligence algorithm for intrusion detection using significant features,” The Scientific World Journal, vol. 2015, pp. 1-16, 2015.
[7] P. Amudha, S. Karthik and S. Sivakumari, “An experimental analysis of hybrid classification approach for intrusion detection,” Indian Journal of Science and Technology, vol. 9, no. 13, 2016.
[8] O. Alomari and Z. A. Othman, “Bees algorithm for feature selection in network anomaly detection,” Journal of Applied Sciences Research, vol. 8, no. 3, pp. 1748-1756, 2012.
[9] M. Aldwairi, Y. Khamayseh and M. Al-Masri, “Application of artificial bee colony for intrusion detection systems,” Security and Communication Networks Security, vol. 8 no. 16, pp. 2730-2740, 2015. 
[10] S. X. Wu and W. Banzhaf, “The use of computational intelligence in intrusion detection systems: A review,” Applied Soft Computing, vol. 10, pp.1–35, 2010.
[11] Y. Wang, G. D. Guo and L .F. Chen, “Chaotic artificial bee colony algorithm: A new approach to the problem of minimization of energy of the 3D protein structure,” Molecular Biology, vol. 47, no. 6, pp. 894–900, 2013.
[12] J. C. Bansal, H. Sharma, K. V. Arya and A. Nagar, “Memetic search in artificial bee colony algorithm,” Soft Computing, vol. 17, no. 10, pp. 1-18, 2013.
[13] V. K. Sharma, R. Kumari and S. Kumar, “Memetic search in artificial bee colony algorithm with fitness based position update,” IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), Jaipur, India, 25 September 2014.
[14] H. Shan, T. Yasuda and K. Ohkura, “A levy flight based hybrid artificial bee colony algorithm for solving numerical optimization problems,” IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 22 September 2014.
[15] K. K. Bharti and P. K. Singh, “Chaotic gradient artificial bee colony for text clustering,” Soft Comput, vol. 20, pp. 1113–1126, 2016.
[16] A. Dastanpour and R. A. R Mahmood, “Feature selection based on genetic algorithm and support vector machine for intrusion detection system,” in Proc of 2nd International Conference on Informatics Engineering & Information Science (ICIEIS2013), pp. 169-181, 2013.
[17] S. M. H. Bamakan, H. Wang, T. Yingjie and Y. Shi, “An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization,” Neuro Computing, vol. 199, pp. 90–102, 2016.
[18] Akashdeep, I. Manzoor and N. Kumar, “A feature reduced intrusion detection system using ANN classifier,” Expert Systems with Applications, vol. 88, pp. 249–257, 2017.
[19] H. Wang, J. Gu and S. Wang, “An effective intrusion detection framework based on SVM with feature augmentation,” Knowledge-Based Systems, vol. 136, pp. 130–139, 2017.
[20] M. R. G. Raman, N. Somu, K. Kirthivasan, R. Liscano and V. S. S. Sriram, “An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine,” Knowledge-Based Systems, vol. 134, pp. 1–12, 2017.  
[21] S. M. H. Bamakan, H. Wang and Y. Shi, “Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem,” Knowledge-Based Systems, vol. 126, pp. 113–126, 2017.
[22] M. R. G. Raman, N. Somu, K. Kirthivasan and V. S. S. Sriram, “A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems,” Neural Networks, vol. 92, pp. 89–97, 2017.
[23] W. K. Mashwani and A. Salhi, “Multiobjective memetic algorithm based on decomposition,” Applied Soft Computing, vol. 21, pp.221–243, 2014.
[24] Z. Zhang, “Efficient computer intrusion detection method based on artificial bee colony optimized kernel extreme learning machine,” Telkomnika Indonesian Journal of Electrical Engineering, vol. 12, no. 3, pp. 1954 -1959, 2014.
[25] R. Singh, H. Kumar and R. K. Singl, “An intrusion detection system using network traffic profiling and online sequential extreme learning machine,” Expert Systems With Applications, vol. 42, pp. 8609–8624, 2015.
[26] S. W. Lin, K. C. Ying, C. Y. Lee and Z. J. Lee, “An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection,” Applied Soft Computing, vol. 12, pp. 3285–3290, 2012.
[27] Y. Chung and N. Wahid, “Hybrid network intrusion detection system using simplified swarm optimization (SSO),” Applied Soft Computing, vol. 12, pp. 3014–3022, 2012.
[28] V.Chahkandi, M. Yaghoobi and G. Veisi, “Feature selection with chaotic hybrid artificial bee colony algorithm based on fuzzy (CHABCF),” Journal of Soft Computing and Applications, vol. 2013, no. 1, pp. 1-8, 2013.
[29] Z. A. Othman, L. M. Theng, S. Zainudin and H. M. Sarim, “Great deluge algorithm feature selection for network intrusion detection,” Journal of Applied Science and Agriculture, vol. 8, no. 4, pp. 322-330, 2013.
[30] M. Gupta and S. K. Shrivastava, “Intrusion detection system based on svm and bee colony,” International Journal of Computer Applications, vol. 111, no. 10, pp. 0975 – 8887, 2015.
[31] Y. Gurcan and A. DoLan, “Angle modulated artificial bee colony algorithms for feature selection,” Applied Computational Intelligence and Soft Computing, vol. 7, pp. 1-6, 2016.
[32] A. A. Aburomman  and M. I. Reaz, “A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems,” Information Sciences, vol. 414, pp. 225–246, 2017.
[33] R. A. R. Ashfaq, X. Z. Wang, J. Z. Huang, H. Abbas and Y. L. He, “Fuzziness based semi-supervised learning approach for intrusion detection system,” Information Sciences, vol. 378, pp. 484–497, 2017.
[34] E. K. Viegas, A. O. Santin and L. S. Oliveira, “Toward a reliable anomaly-based intrusion detection in real-world environments,” Computer Networks, vol. 127, pp. 200–216, 2017.
[35] D. Karaboga and B. Akay, “A comparative study of artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, pp. 108–132, 2009.
[36] J. P. Nolan, Stable Distributions, Models for Heavy Tailed Data, Math/Stat Department American University, 2015.
 [37] زینب صادقی چوینلی و سید محمد حسین معطر، «زمان‌بندی سیستم‌های تولید کارگاهی انعطاف‌پذیر با استفاده از الگوریتم جستجوی فاخته بهبودیافته با خوشه‌بندی مارکوف و پرواز لوی»،  مجله مهندسی برق دانشگاه تبریز، دوره 46، شماره 4، صفحه 185-193، زمستان 1395.
[38] R. Jensi and J. G. Wiselin, “An enhanced particle swarm optimization with levy flight for global optimization,” Applied Soft Computing, vol. 43, pp. 248–261, 2016.
[39] L. Dhanabal and S. P. Shantharajah, “A study on NSL-KDD dataset for intrusion detection system based on classification algorithms,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 6, 2015.

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