سیستم تشخیص نفوذ بهبود یافته مبتنی بر الگوریتم ژنتیک خود تطبیق جزیره ای برای حل ماشین بردار پشتیبان به صورت یادگیری چندهسته ای با کد کننده های خودکار
محورهای موضوعی : انرژی های تجدیدپذیر
الهه فقیه نیا
1
,
سید رضا کامل طباخ فریضنی
2
,
مریم خیرآبادی
3
1 - دانشکده مهندسی- واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران
2 - دانشکده مهندسی- ,واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
3 - دانشکده مهندسی- واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران
کلید واژه: ماشین بردار پشتیبان, الگوریتم ژنتیک جزیره‌ای, سیستم‌های تشخیص نفوذ, کلان داده‌ها, الگوریتم ژنتیک خود تطبیق,
چکیده مقاله :
نفوذ به سیستم‌ها از طریق زیرساخت شبکه و اینترنت یکی از چالش‌های امنیتی است که دنیای فناوری اطلاعات و ارتباطات را با آن روبرو کرده است و می‌تواند منجر به تخریب سیستم‌ها و دسترسی به داده‌ها و اطلاعات گردد. در این مقاله یک مدل ماشین بردار پشتیبان که هسته‌های آن وزن‌دار شده به همراه پارامترهای هسته‌های ماشین بردار پشتیبان برای سیستم تشخیص نفوذ ارائه شده است. با توجه به پیچیدگی محاسباتی این مدل، روش الگوریتم ژنتیک جزیره‌ای پویای خود تطبیقی پیشنهاد شده تا پیچیدگی محاسبات را کم نماید. در این روش از اتوانکودر نیز برای کاهش حجم داده‌ها استفاده شده است. روش پیشنهادی یک روش ترکیبی پیشنهادی مبتنی بر اتوانکودر و ماشین بردار پشتیبان بهبودیافته با الگوریتم ژنتیک جزیره‌ای پویای خود تطبیق است که دقت بهتری در مسائل تشخیص نفوذ را نشان می دهد. نتایج شبیه سازی بر روی مجموعه داده DARPA برای تست عملکرد مورد استفاده قرار گرفته است.
Intrusion into systems through network infrastructure and the Internet is one of the security challenges facing the world of information and communication technology and can lead to the destruction of systems and access to data and information. In this paper, a support vector machine model with weighted and parameters of SVM kernels are presented to detect the intrusion. Due to the high complexity of this problem, conventional optimization methods are not able to solve it. Therefore, we propose a Distributed Self Adaptive Genetic Algorithm (DSAGA). On the other hand, due to the high volume of data in such issues, Auto encoder has been used to reduce data. The proposed approach is a hybrid method based on Auto encoder, improved Support Vector Machine and Distributed Self Adaptive Genetic Algorithm (DSAGA) that it is evaluated by its execution on DARPA data set.
[1] A. Almomani, M. Alauthman, F. Albalas, O. Dorgham, A. Obeidat "An online intrusion detection system to cloud computing based on NeuCube algorithms", International Journal of Cloud Applications and Computing, vol. 8, no. 2, pp.1042-1059, 2018 (doi:10.4018/IJCAC.2018040105).
[2] S. A. Mulay, P. Devale, G. Garje, "Intrusion detection system using support vector machine and decision tree", International Journal of Computer Applications,vol. 3, no.3, pp. 40-43, 2010 (doi:10.5120/758-993).
[3] W. Laftah Al-Yaseen,"Improving intrusion detection system by developing feature selection model based on firefly algorithm and support vector machine", IAENG International Journal of Computer Science, vol. 46, no. 4, pp. 534-540, 2019 (doi: IJCS_46_4_04).
[4] M. R. G. Raman, N. Somu, S. Jagarapu, T. Manghnani, T. Selvam, K. Krithivasan,V. S. S. Sriram, "An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm", Artificial Intelligence Review, vol. 53, pp. 3255-3286, 2019 (doi:10.1007/s.10462-019-09762-z).
[5] M. Ramkumar, M. Manikandan, K. Sathish Kumar, R. K. Kumar, "Intrusion detection in manets using support vector machine with ant colony optimization" ICTACT journals on data csience and machine learning, vol. 1, no.1, 2019.
[6] J. C. Badajena, C. Rout, "Incorporating hidden markov model into anomaly detection technique for network intrusion detection", International Journal of Computer Applications, vol. 53, no. 11, 2012 (doi: 10.5120/8469-2395).
[7] P. Dorogovs, A. Borisov, A. Romanovs, "Building an intrusion detection system for it security based on data mining techniques", Applied Computer Systems, vol. 45, no. 1, pp. 43-48, 2011 (doi: 10.2478/v10143-011-0040-3).
[8] S. Shirbhate, S. Sherekar and, V. Thakare, " Performance evaluation of PCA filter in clustered based intrusion detection system", Proceeding of the IEEE/ICESC, pp. 217-221, Nagpur, India ,Feb. 2014 (doi: 10.1109/ICESC.2014.100).
[9] D. Gupta, S. Singhal, S. Malik, A. Singh, "Network intrusion detection system using various data mining techniques", Proceeding of the IEEE/(RAINS), pp. 1-6, Bangalore, India, May. 2016 (doi: 10.1109/RAINS.2016.7764418).
[10] E. Ariafar, R. Kiani, "Intrusion detection system using an optimized framework based on datamining techniques", Proceeding of the IEEE/KBEI, pp. 0785-0791, Tehran, Iran, Dec. 2017 (doi: 10.1109/KBEI.2017.8324903).
[11] J. A. Sukumar, I. Pranav,MM. Neetish, J. Narayanan, "Network intrusion detection using improved genetic k-means algorithm", Proceeding of the IEEE/ICACCI, pp. 2441-2446, Bangalore, India, Sept. 2018 (doi: 10.1109/ICACCI.2018.8554710).
[12] P. S. Bhattacharjee, A. K. M, Fujail, A. A. Begum, "Intrusion detection system for NSL-KDD data set using vectorised fitness function in genetic algorithm", Advances in Computational Sciences and Technolog.,vol. 10, no. 2, pp. 235-246, 2017.
[13] J. Ghasemi, J. Esmaeily, R. Moradinezhad, "Intrusion detection system using an optimized kernel extreme learning machine and efficient features", Sådhanå,vol. 45, no. 2, pp.1-9, 2020(doi: 10.1007/s12046-019-1230-x).
[14] D. Pal, A. Parashar, "Improved genetic algorithm for intrusion detection system", Proceeding of the IEEE/CICN, pp. 835-839, Bhopal, India, Nov. 2014 (doi: 10.1109/CICN.2014.178).
[15] Y. Danane, T. Parvat, "Intrusion detection system using fuzzy genetic algorithm", Proceeding of the IEEE/ ICPC , pp. 1-5, St. Louis, Missouri, USA, March. 2015 (doi: 10.1109/PERVASIVE.2015.7086963).
[16] A. F. A. Pinem, E. B. Setiawan, "Implementation of classification and regression tree (CART) and fuzzy logic algorithm for intrusion detection system", Proceeding of the IEEE/ ICoICT, pp. 266-271, Bali, Indonesia, May. 2015 (doi: 10.1109/ICoICT.2015.7231434).
[17] S. Sahu, B. M. Mehtre, "Network intrusion detection system using J48 decision tree", Proceeding of the IEEE/ ICACCI, pp. 2023-2026, Kochi, India, Aug. 2015 (doi: 10.1109/ICACCI.2015.7275914).
[18] S. M. H. Bamakan, H. Wang, 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 (doi: 10.1016/j.knosys.2017.03.012).
[19] C. A. Catania, C. G. Garino, "Automatic network intrusion detection: Current techniques and open issues", Computers & Electrical Engineering, vol. 38, no. 5, pp. 1062-1072, 2012 (doi: 10.1016/j.compeleceng.2012.05.013).
[20] S. Aljawarneh, M. Aldwairi, M. B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model", Journal of Computational Science, vol. 25, pp. 152-160, 2018 (doi: 10.1016/j.jocs.2017.03.006).
[21] G. Sandhya, A. Julian, "Intrusion detection in wireless sensor network using genetic K-means algorithm", Proceeding of the IEEE/ ICACCCT, pp. 1791-1794, Ramanathapuram, India, May. 2014 (doi: 10.1109/ICACCCT.2014.7019418).
[22] M. Sharma, K. Jindal, A.Kumar, "Intrusion detection system using Bayesian approach", International Journal of Computer Application, vol. 48, no.5, pp. 29-33,2012.
[23] G. Sandhya, A. Julian, "Intrusion detection in wireless sensor network using genetic K-means algorithm", Proceeding of the IEEE/ ICACCCT pp. 1791-1794, Ramanathapuram, India, May. 2014 (doi: 10.1109/ICACCCT.2014.7019418).
[24] T. Yerong, S. Sai, X. Ke, L. Zhe, "Intrusion detection based on support vector machine using heuristic genetic algorithm", Proceeding of the IEEE/CSNT, pp. 681-684, Bhopal, India,Apr. 2014 (doi: 10.1109/CSNT.2014.143).
[25] Q. Schueller, K. Basu, M. Younas, M. Patel, F. Ball, "A hierarchical intrusion detection system using support vector machine for SDN network in cloud data center", Proceeding of the IEEE/ITNAC), pp. 1-6. Sedney, Australia, Nov. 2018 (doi: 10.1109/ATNAC.2018.8615255).
[26] R. Vijayanand, D. Devaraj, B. Kannapiran, "Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection", Computers and Security, vol. 77, pp. 304-314, 2018 (doi: doi.org/10.1016/j.cose.2018.04.010).
[27] M. G. Raman, N. Somu, K. Kirthivasan, R. Liscano, 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, 2011 (doi: 10.1016/j.knosys.2017.07.005).
[28] S. Mirjalili, "Genetic algorithm", Evolutionary Algorithms and Neural Networks, Part of the Studies in Computational Intelligence Book Series (SCI), vol.780, pp. 43-55, 2019 (doi: 10.1007/978-3-319-93025-1_4)
[29] M. Gharaibeh, C. Papadopoulos, "Darpa-2009 intrusion detection dataset report", Tech. Rep., 2014.
_||_[1] A. Almomani, M. Alauthman, F. Albalas, O. Dorgham, A. Obeidat "An online intrusion detection system to cloud computing based on NeuCube algorithms", International Journal of Cloud Applications and Computing, vol. 8, no. 2, pp.1042-1059, 2018 (doi:10.4018/IJCAC.2018040105).
[2] S. A. Mulay, P. Devale, G. Garje, "Intrusion detection system using support vector machine and decision tree", International Journal of Computer Applications,vol. 3, no.3, pp. 40-43, 2010 (doi:10.5120/758-993).
[3] W. Laftah Al-Yaseen,"Improving intrusion detection system by developing feature selection model based on firefly algorithm and support vector machine", IAENG International Journal of Computer Science, vol. 46, no. 4, pp. 534-540, 2019 (doi: IJCS_46_4_04).
[4] M. R. G. Raman, N. Somu, S. Jagarapu, T. Manghnani, T. Selvam, K. Krithivasan,V. S. S. Sriram, "An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm", Artificial Intelligence Review, vol. 53, pp. 3255-3286, 2019 (doi:10.1007/s.10462-019-09762-z).
[5] M. Ramkumar, M. Manikandan, K. Sathish Kumar, R. K. Kumar, "Intrusion detection in manets using support vector machine with ant colony optimization" ICTACT journals on data csience and machine learning, vol. 1, no.1, 2019.
[6] J. C. Badajena, C. Rout, "Incorporating hidden markov model into anomaly detection technique for network intrusion detection", International Journal of Computer Applications, vol. 53, no. 11, 2012 (doi: 10.5120/8469-2395).
[7] P. Dorogovs, A. Borisov, A. Romanovs, "Building an intrusion detection system for it security based on data mining techniques", Applied Computer Systems, vol. 45, no. 1, pp. 43-48, 2011 (doi: 10.2478/v10143-011-0040-3).
[8] S. Shirbhate, S. Sherekar and, V. Thakare, " Performance evaluation of PCA filter in clustered based intrusion detection system", Proceeding of the IEEE/ICESC, pp. 217-221, Nagpur, India ,Feb. 2014 (doi: 10.1109/ICESC.2014.100).
[9] D. Gupta, S. Singhal, S. Malik, A. Singh, "Network intrusion detection system using various data mining techniques", Proceeding of the IEEE/(RAINS), pp. 1-6, Bangalore, India, May. 2016 (doi: 10.1109/RAINS.2016.7764418).
[10] E. Ariafar, R. Kiani, "Intrusion detection system using an optimized framework based on datamining techniques", Proceeding of the IEEE/KBEI, pp. 0785-0791, Tehran, Iran, Dec. 2017 (doi: 10.1109/KBEI.2017.8324903).
[11] J. A. Sukumar, I. Pranav,MM. Neetish, J. Narayanan, "Network intrusion detection using improved genetic k-means algorithm", Proceeding of the IEEE/ICACCI, pp. 2441-2446, Bangalore, India, Sept. 2018 (doi: 10.1109/ICACCI.2018.8554710).
[12] P. S. Bhattacharjee, A. K. M, Fujail, A. A. Begum, "Intrusion detection system for NSL-KDD data set using vectorised fitness function in genetic algorithm", Advances in Computational Sciences and Technolog.,vol. 10, no. 2, pp. 235-246, 2017.
[13] J. Ghasemi, J. Esmaeily, R. Moradinezhad, "Intrusion detection system using an optimized kernel extreme learning machine and efficient features", Sådhanå,vol. 45, no. 2, pp.1-9, 2020(doi: 10.1007/s12046-019-1230-x).
[14] D. Pal, A. Parashar, "Improved genetic algorithm for intrusion detection system", Proceeding of the IEEE/CICN, pp. 835-839, Bhopal, India, Nov. 2014 (doi: 10.1109/CICN.2014.178).
[15] Y. Danane, T. Parvat, "Intrusion detection system using fuzzy genetic algorithm", Proceeding of the IEEE/ ICPC , pp. 1-5, St. Louis, Missouri, USA, March. 2015 (doi: 10.1109/PERVASIVE.2015.7086963).
[16] A. F. A. Pinem, E. B. Setiawan, "Implementation of classification and regression tree (CART) and fuzzy logic algorithm for intrusion detection system", Proceeding of the IEEE/ ICoICT, pp. 266-271, Bali, Indonesia, May. 2015 (doi: 10.1109/ICoICT.2015.7231434).
[17] S. Sahu, B. M. Mehtre, "Network intrusion detection system using J48 decision tree", Proceeding of the IEEE/ ICACCI, pp. 2023-2026, Kochi, India, Aug. 2015 (doi: 10.1109/ICACCI.2015.7275914).
[18] S. M. H. Bamakan, H. Wang, 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 (doi: 10.1016/j.knosys.2017.03.012).
[19] C. A. Catania, C. G. Garino, "Automatic network intrusion detection: Current techniques and open issues", Computers & Electrical Engineering, vol. 38, no. 5, pp. 1062-1072, 2012 (doi: 10.1016/j.compeleceng.2012.05.013).
[20] S. Aljawarneh, M. Aldwairi, M. B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model", Journal of Computational Science, vol. 25, pp. 152-160, 2018 (doi: 10.1016/j.jocs.2017.03.006).
[21] G. Sandhya, A. Julian, "Intrusion detection in wireless sensor network using genetic K-means algorithm", Proceeding of the IEEE/ ICACCCT, pp. 1791-1794, Ramanathapuram, India, May. 2014 (doi: 10.1109/ICACCCT.2014.7019418).
[22] M. Sharma, K. Jindal, A.Kumar, "Intrusion detection system using Bayesian approach", International Journal of Computer Application, vol. 48, no.5, pp. 29-33,2012.
[23] G. Sandhya, A. Julian, "Intrusion detection in wireless sensor network using genetic K-means algorithm", Proceeding of the IEEE/ ICACCCT pp. 1791-1794, Ramanathapuram, India, May. 2014 (doi: 10.1109/ICACCCT.2014.7019418).
[24] T. Yerong, S. Sai, X. Ke, L. Zhe, "Intrusion detection based on support vector machine using heuristic genetic algorithm", Proceeding of the IEEE/CSNT, pp. 681-684, Bhopal, India,Apr. 2014 (doi: 10.1109/CSNT.2014.143).
[25] Q. Schueller, K. Basu, M. Younas, M. Patel, F. Ball, "A hierarchical intrusion detection system using support vector machine for SDN network in cloud data center", Proceeding of the IEEE/ITNAC), pp. 1-6. Sedney, Australia, Nov. 2018 (doi: 10.1109/ATNAC.2018.8615255).
[26] R. Vijayanand, D. Devaraj, B. Kannapiran, "Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection", Computers and Security, vol. 77, pp. 304-314, 2018 (doi: doi.org/10.1016/j.cose.2018.04.010).
[27] M. G. Raman, N. Somu, K. Kirthivasan, R. Liscano, 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, 2011 (doi: 10.1016/j.knosys.2017.07.005).
[28] S. Mirjalili, "Genetic algorithm", Evolutionary Algorithms and Neural Networks, Part of the Studies in Computational Intelligence Book Series (SCI), vol.780, pp. 43-55, 2019 (doi: 10.1007/978-3-319-93025-1_4)
[29] M. Gharaibeh, C. Papadopoulos, "Darpa-2009 intrusion detection dataset report", Tech. Rep., 2014.