شناسایی جریان‌های مخرب در شبکه با به‌کارگیری اجماع

   [1]      R. Talabis, “Honeynet learning: discovering IT security,” SIGCSE Bull, vol. 38, pp. 110-114, 2006.##

   [2]      S. Hido and H. Kashima, “Roughly balanced bagging for imbalanced data,” Statistical Analysis and Data Mining: the ASA Data Science, vol. 5, pp. 143-152, 2009.##

   [3]      P. Kang and S. Cho, “Ensemble of under-sampled SVMs for data imbalance problems,” Lecture Notes in Computer Science, vol. 4232, pp. 837–846, 2006.##

   [4]      I. Corona, G. Giacinto, C. Mazzariello, F. Roli, and C. Sansone, “Information fusion for computer security: state of the art and open issues,” Information Fusion, vol. 10, no. 4, pp. 274–284, 2009.##

   [5]      J. Z. Kolter and M. A. Maloof, “Learning to detect and classify malicious executables in the wild,” Journal of Machine Learning Research, vol. 7, pp. 2721–2744, 2006.##

   [6]      M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo, “Data mining methods for detection of new malicious executables,” In Proceedings of the IEEE Symposiumon Security and Privacy, pp. 38–49, 2001.##

   [7]      G. Giacinto, R. Perdisci, M. D. Rio, and F. Roli, “Intrusion detection in computer networks by a modular ensemble of one-class classifiers,” Information Fusion, vol. 9, no. 1, pp. 69–82, 2008.##

   [8]      G. Giacinto, F. Roli, and L. Didaci, “Fusion of  multiple classifiers for intrusion detection in computer networks,” Pattern Recognition Letters, vol. 24, no. 12, pp.  1795–1803, 2003.##

   [9]       B. Zhang, J. Yin, S. Wang, and X. Yan, “Research on virus detection technique based on ensemble neural network and SVM,” Advanced Intelligent Computing Theories and Methodologies, vol. 137, pp. 24-33, 2014.##

[10]      S. M. AbdElrahman and A. Abraham, “Intrusion detection using error correcting output code based ensemble,” International Conference Hybrid Intelligent Systems (HIS), IEEE, pp. 181-186, 2014.##

[11]      M. Sreenath and J. Udhayan, “Intrusion detection system using Bagging Ensemble Selection,” International Conference Engineering and Technology (ICETECH), pp. 1-4, 2015.##

[12]      D. P. Gaikwad and R. C. Thool, “Intrusion detection System using bagging ensemble method of machine learning,” International Conference Computing Communication Control and Automation (ICCUBEA), pp. 291-295, 2015.##

[13]      P. Sornsuwit and S. Jaiyen, “Intrusion detection model based on ensemble learning for U2R and R2L attacks,” International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 354-359, 2015.##

[14]      S. Masarat, H. Taheri, and S. Sharifian, “A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems,” International Conference Computer and Knowledge Engineering (ICCKE), pp.     165-170, 2014.##

[15]      M. Milliken, Y. Bi, L. Galway, and G. Hawe, “Ensemble learning utilising feature pairings for intrusion detection,” World Congress on Internet Security (WorldCIS), pp. 24-31, 2015.##

[16]      A. A. Aburomman and M. B. IbneReaz, “A novel SVM-kNN-PSO ensemble method for intrusion detection system,” Applied Soft Computing, vol. 38, pp. 360–372, 2016.##

[17]      R. Singha, H. Kumarb, and R. K. Singlac, “An intrusion detection system using network traffic profiling and online sequential extreme learning machine,” Expert Systems with Applications, vol. 42, pp. 8609–8624, 2015.##

[18]      R. M. Elbasiony, E. A. Sallam, T. E. Eltobely, and M. M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means,” Ain Shams Eng. J, vol. 4, pp. 753–762, 2013.##

[19]      D. Watson and J. Riden, “The honeynet project,” Technical Report, 2006.##

[20]      Members of the  Honeynet Project, “Know Your Enemy: Learning about Security Threats,” 2nd edn.             Addison-Wesley, Boston, 2004.##

[21]      N. Provos and T. Holz, “Virtual honeypots: from botnet tracking to intrusion detection,” 1st edn. Addison-Wesley Professional, Boston, 2007.##

[22]      L. Rokach, “Ensemble-based classifiers,’ Artificial Intelligence Review, vol. 33, pp. 1–39, 2010.##

[23]      R. Polikar, “Ensemble based systems in decision making,” Circuits and Systems Magazine, vol. 6, pp. 21–45, 2006.##

[24]      D. Opitz and R. Maclin, “Popular ensemble methods: An empirical study,” Journal of Artificial Intelligence Research, vol. 11, pp. 169–198, 1999.##

[25]      L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993–1001, 1990.##

[26]      R. E. Schapire, “The strength of weak learnability,” Machine Learning, vol. 5, no. 2, pp. 197–227, 1990.##

[27]      Z. H. Zhou, “Ensemble methods: foundations and algorithms. machine learning & pattern recognition series,” Chapman & Hall/CRC, Boca Raton FL, 2012.##

[28]      B. M. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M.J. Golkar, and A. Ebrahimi, “A hybrid method consisting of GA and SVM for intrusion detection system,” Neural Computing and Applications, pp. 1–8, 2015.##

[29]      S. Rastegari, P. Hingston, and C. P. Lam, “Evolving statistical rulesets for network intrusion detection,” Applied Soft Computing, vol. 33, pp. 348–359, 2015.##

[30]      R. Ghorbani and H. Abrishami, “Using Stereo Vision to Provide a Vision-Based Augmented Reality System,” Tabriz Journal of Electrical Eng., vol. 42, no. 1, 2012. (In Persian)##

[31]      S. Abdollahzadeh, M. A. Balafar, and L. Mohammad Khanli, “|Using Clustering and Markov Model in Predicting Web Users' Next Request,” Tabriz Journal of Electrical Eng., vol. 45, no. 3, 2014. (In Persian)##

[32]      F. H. Abbasi, R. J. Harris, S. Marsland, and G. Moretti, “An Exemplar-Based Learning Approach for Detection and Classification of Malicious Network Streams in Honeynets,” Security and Communication Networks, vol. 7, no. 2, pp. 352-364, 2014.##

 [33]      H. Parvin, S. Ansari, and S. Parvin, “Proposing a New Method for Non-Relative Imbalanced Dataset,” Soft Computing Models in Industrial and Environmental Applications, vol. 188, pp. 297-306, 2013.##

[34]      H. Parvin, B. Minaei, H. Alinejad-Rokny, and W. Punch, “Data weighing mechanisms for clustering ensembles,” Computers and Electrical Engineering, vol. 5. no. 39, pp. 1433–1450, 2013.##

[35]      R. K. Shahzad and N. Lavesson, “Comparative Analysis of Voting Schemes for Ensemble-based Malware Detection,” Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 4, no. 1, pp. 98-117, 2013.##

[36]      A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys and Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.##

[37]      A. A. Aburomman and M. B. Ibne Reaz, “A novel SVM-Knnpso ensemble method for intrusion detection system,” Applied Soft Computing Journal, vol. 38, pp. 360–372, 2016.##

[38]      M. Li, S. Pan, Y. Zhang, and X. Cai, “Classifying networked text data with positive and unlabeled examples,” Pattern Recognition Letters, vol. 77, pp. 1–7, 2016.##

[39]      S. Parsa and M. Zeinipour, “Botnet Detection with Flow Behavior Analysis Approach,” Journal of Electrical & Cyber Defence, vol. 5, no. 4, pp. 35-50, 2017. (In Persian)##