[1] S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, & M. Conti, “Employing Program Semantics for Malware Detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2591-2604, 2015.
[2] Z. Bazrafshan, H. Hashemi, S. M. H. Fard, & A. Hamzeh, “A survey on heuristic malware detection techniques,” in IKT 2013 5th Conference on Information, 2013.
[3] A. Damodaran, F. D. Troia, C. A. Visaggio, T. H. Austin, & M. Stamp, “A comparison of static, dynamic, and hybrid analysis for malware detection,” J. comput. virol. hacking tech., vol. 13, no. 1, pp. 1-12, 2017.
[4] M. Ahmadi, A. Sami, H. Rahimi, & B. Yadegari, “Malware detection by behavioural sequential patterns,” Comput. fraud secur., vol. 2013, no. 8, pp. 11-19, 2013.
[5] H. Darabian, A. Dehghantanha, S. Hashemi, S. Homayoun, & K.-K. R. Choo, “An opcode based technique for polymorphic Internet of Things malware detection: An OpCode-Based Technique for Polymorphic Internet of Things Malware Detection,” Concurr. Comput., vol. 32, no. 6, pp. 51-73, 2020.
[6] B. B. Rad, M. Masrom, & S. Ibrahim, “Opcodes histogram for classifying meta-morphic portable executables malware,” in 2012 International Conference on e-Learning and e-Technologies in Education (ICEEE), IEEE, pp. 209-213, 2012.
[7] W. Hardy, L. Chen, S. Hou, Y. Ye, & X. Li, “DL 4 MD: A deep learning framework for intelligent malwarc detection,” in Inel Conf. Data Mining, CSREA Press, pp. 61-67, 2016.
[8] L. Yu, S. Wang, & K. K. Lai, “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm,” Energy Econ., vol. 30, no. 5, pp. 2623-2635, 2008.
[9] S. Almarri & P. Sant, “Optimised Malware Detection in Digital Forensics,” Int. j. netw. secur. appl., vol. 6, no. 1, pp. 1-15, 2014.
[10] M. Deypir, “Entropy-based security risk measurement for Android mobile applications,” Soft Comput., vol. 23, no. 16, pp. 7303-7319, 2019.
[11] Ram Mahesh Yadav, “Effective analysis of malware detection in cloud computing,” Computers & Security, vol. 83, pp. 14-21, 2019.
[12] M. Ghasabi, M. Deypir, & E. Mahdipour, "A New Algorithm Based on Hellinger Distance for Mitigation of DDoS Attacks in Software Defined Networks," Journal of Electronical & Cyber Defence, Vol. 5, No. 4, 2017.(In Persian)
[13] N. Usman, S. Usman, F. Khan, M. A., Jan, A. Sajid, M. Alazab, & P. Watters, "Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics," Future Generation Computer Systems, vol. 118, pp. 124-141, 2021.
[14] A. Shahraki, M. Abbasi, & Ø. Haugen, “Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost,” Eng. Appl. Artif. Intell., vol. 94, no. 5, pp. 103-770, 2020.
[15] L. Pallippattu Mathai, "Malware Detection on Android using Adaboost Algorithm," Doctoral dissertation, Dublin, National College of Ireland, 2021.
[16] F. C. Garcia & F. P. Muga II, "Random forest for malware classification," arXiv preprint arXiv:1609.07770, 2016.
[17] C. Galen & Steele, R. “Performance Maintenance Over Time of Random Forest-based Malware Detection Models,” 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 536-541, 2020.
[18] S. Joshi, H. Upadhyay, L. Lagos, N. S. Akkipeddi, & V. Guerra, "Machine learning approach for malware detection using random forest classifier on process list data structure," In Proceedings of the 2nd International Conference on Information System and Data Mining, pp. 98-102, 2018.
[19] H. D. Pham, T. D. Le, & T. N. Vu, "Static PE malware detection using gradient boosting decision trees algorithm," In International Conference on Future Data and Security Engineering, pp. 228-236, Springer, Cham, November 2018.
[20] C. Galen & R. Steele, "Empirical Measurement of Performance Maintenance of Gradient Boosted Decision Tree Models for Malware Detection," In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 193-198, 2021.
[21] J. E. L. Abdelkhalki, M. B. Ahmed, & A. A. Boudhir, “Image malware detection using deep learning,” IJCNIS, vol. 12, no. 2, 2020.
[22] D. Tian, Q. Ying, X. Jia, R. Ma, C. Hu, & W. Liu, “MDCHD: A novel malware detection method in cloud using hardware trace and deep learning,” Computer Networks, vol. 198, pp. 108-394, 2021.
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