-
آرشیو :
نسخه بهار 1400
-
نوع مقاله :
پژوهشی
-
شناسه دیجیتال (DOI) :
10.22034/RCSJ.2021.0725.0509
-
کد پذیرش :
1382
-
موضوع :
مهندسی نرم افزار
-
نویسنده/گان :
معصومه مرادی، مجتبی صالحی
-
کلید واژه :
بدافزار، تشخیص، یادگیری ماشین، پردازش تصویر.
-
مراجع :
[1] شیرازی، حسین.، فرشچی، سیدمحمدرضا. 1393. ارائه یک روش جدید برای شناسایی بدافزارها در سطح مجازی ساز در ماشین های مجازی. پدافند الکترونیکی و سایبری: 7(2): 23-34.
[2] رنجی، هادی، و پارسا، سعید. 1397. شناسائی بدافزارها با استفاده از تصویرسازی. فصلنامه پدافند غیرعامل, 9(2): 101-95.
[3] Gibert, D., 2016. Convolutional neural networks for malware classification. University Rovira i Virgili, Tarragona, Spain.
[4] Gupta, A., Kuppili, P., Akella, A. and Barford, P., 2009, January. An empirical study of malware evolution. In 2009 First International Communication Systems and Networks and Workshops (pp. 1-10). IEEE.
[5] Ma, J., Dunagan, J., Wang, H.J., Savage, S. and Voelker, G.M., 2006, October. Finding diversity in remote code injection exploits. In Proceedings of the 6th ACM SIGCOMM conference on Internet measurement (pp. 53-64).
[6] Mercaldo, F., Di Sorbo, A., Visaggio, C.A., Cimitile, A. and Martinelli, F., 2018. An exploratory study on the evolution of Android malware quality. Journal of Software: Evolution and Process, 30(11), p.e1978.
[7] Ouellette, J., Pfeffer, A. and Lakhotia, A., 2013, October. Countering malware evolution using cloud-based learning. In 2013 8th International Conference on Malicious and Unwanted Software:" The Americas"(MALWARE) (pp. 85-94). IEEE.
[8] Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E. and Ahmadi, M., 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135.
[9] WebSite, 2020. Available at: https://sarvamblog.blogspot.com/2014/08/supervised-classification-with-k-fold.html
[10] Zakaria, M., Al-Shebany, M. and Sarhan, S., 2014. Artificial neural network: a brief overview. Int J Eng Res Appl, 4, pp.7-12.
[11] Zha, H., He, X., Ding, C., Gu, M. and Simon, H.D., 2002. Spectral relaxation for k-means clustering. In Advances in neural information processing systems (pp. 1057-1064).
[12] Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E. and Ahmadi, M., 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135.
[13] Saxe, J. and Berlin, K., 2015, October. Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE) (pp. 11-20). IEEE.
[14] Kramer, S. and Bradfield, J.C., 2010. A general definition of malware. Journal in computer virology, 6(2), pp.105-114.
[15] Forrest, S., Hofmeyr, S.A., Somayaji, A. and Longstaff, T.A., 1996, May. A sense of self for unix processes. In Proceedings 1996 IEEE Symposium on Security and Privacy (pp. 120-128). IEEE.
[16] Wang, K. and Stolfo, S.J., 2004, September. Anomalous payload-based network intrusion detection. In International workshop on recent advances in intrusion detection (pp. 203-222). Springer, Berlin, Heidelberg.
[17] Kalash, M., Rochan, M., Mohammed, N., Bruce, N.D., Wang, Y. and Iqbal, F., 2018, February. Malware classification with deep convolutional neural networks. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE.
[18] Makandar, A. and Patrot, A., 2015, December. Malware analysis and classification using artificial neural network. In 2015 International conference on trends in automation, communications and computing technology (I-TACT-15) (pp. 1-6). IEEE.
[19] Idika, N. and Mathur, A.P., 2007. A survey of malware detection techniques. Purdue University, 48, pp.2007-2.
[20] Elhadi, A.A., Maarof, M.A. and Osman, A.H., 2012. Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), p.283.
[21] Tang, A., Sethumadhavan, S. and Stolfo, S.J., 2014, September. Unsupervised anomaly-based malware detection using hardware features. In International Workshop on Recent Advances in Intrusion Detection (pp. 109-129). Springer, Cham.
[22] Landage, J. and Wankhade, M.P., 2013. Malware and malware detection techniques: A survey. International Journal of Engineering Research and Technology (IJERT), 2(12), pp.2278-0181.
- صفحات : 14-28
-
دانلود فایل
( 770.33 KB )