ارائه راه‌کاری کارا برای تشخیص بدافزارهای ناشناخته بر مبنای تحلیل دستورات اسمبلی

[1]      S. Mansfield-Devine, “Security guarantees: building credibility for security vendors”, Network Security, vol. 2016, no. 2, pp. 14-18, 2016.

[2]      G. McGraw, G. Morrisett, “Attacking malicious code: A report to the infosec research council “, IEEE Software, pp 33–44, 2000.

[3]      J. Jang, H. Kang, J. Woo, A. Mohaisen, H. KangKim, “Andro-Dumpsys: Anti-malware system based on the similarity of malware creator and malware centric information”, Computers & Security, vol. 58, pp. 125-138, 2016.

[4]      Z. Bazrafshan, H. Hashemi, S. Fard, A. Hamzeh, “A survey on heuristic malware detection techniques”, 5th Conference on Information and Knowledge Technology (IKT), pp. 113–120, shiraz, 2013.

[5]      H. Hashemi, A. Azmoodeh, A. Hamzeh and S. Hashemi “Graph embedding as a new approach for unknown malware detection”, Computer Virology and Hacking Techniques, vol. 12, no. 4, pp. 101-141, 2016.

[6]      P. Vinod, R. Jaipur, V. Laxmi, M. Gaur, “Survey on malware detection methods”, Proceedings of the 3rd Hackers’ Workshop on computer and internet security (IITKHACK’09), pp. 74-79, 2009.

[7]      M. Xu et aI., “A similarity metric method of obfuscated malware using fimction-call graph”, Journal in Computer Virology, vol. 9, no. 1, pp. 35- 47, 2013.

[8]      I. Santos, F. Brezo, X. Ugarte-Pedrero, and P. G. P. Bringas, “Opcode sequences as representation of executables for data-mining-based unknown malware detection”, information Sciences, vol. 231, pp. 64–82, 2013.

[9]      Y. Yanfang, L. Tao, J. Qingshan, W. Youyu, “CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 3, pp. 298-307, 2010.

[10]      A. Shabtai, R. Moskovitch, Y. Elovici, C. Glezer, “Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey”, Information Security Technical Report, vol. 14, no. 1, pp. 16-29, 2009.

[11]      B. Kinholkar, “Study of Dataset Feature Filtering of OpCode for Malware Detection Using SVM Training Phase”, International Journal of Science and Research (IJSR), vol. 4, no. 12, 2015.

[12]      S. Cesare, Y. Xiang, W. Zhou, “Control flow-based malware variant detection”, IEEE Transactions on Dependable and Secure Computing, pp. 307–317, 2014.

[13]      Y. Ding, W. Dai, Sh. Yan, Y. Zhang, “Control flow-based opcode behavior analysis for Malware detection”, Computer & Security, vol. 44, pp. 65-74, 2014.

[14]      T. Wüchner, M. Ochoa, A. Pretschner, “Malware detection with quantitative data flow graphs”, 9th ACM symposium on Information, computer and communications security, pp. 271–282, 2014.

[15]      T. Abou-assaleh, N. Cercone, V. Keselj, R. Sweidan, “N-grambased detection of new malicious code”, Computer Software and Applications Conference, Proceedings of the 28th Annual International, vol. 2, no. 1, pp. 41–42, 2004.

[16]      G. Canfora, A. Lorenzo, “Effectiveness of opcode ngrams for detection of multi family android malware”, 10th International Conference on Availability, Reliability and Security (ARES), pp. 333–340. IEEE, 2015.

[17]      B. Kang, S. Y. Yerima, K. Mclaughlin and S. Sezer, “N-opcode analysis for android malware classification and categorization”, Cyber Security and Protection of Digital Services (Cyber Security), London, 2016.

[18]      D. Uppal, R. Sinha, “Exploring behavioral aspects of API calls for malware identification and categorization”, In 2014 International Conference on Computational Intelligence and Communication Networks, pp. 824-828, 2014.

[19]      Y. Qiu, “The openness of Open Application Programming Interfaces”, information, Communication & Society, pp. 1-17, 2016.

[20]      M. Belaoued, S. Mazouzi, “An MCA Based Method for API Association Extraction for PE Malware Categorization”, International Journal of Information and Electronics Engineering, vol. 5, no. 3, pp. 225-231, 2015.

[21]      D. Bilar, “Opcodes as predictor for malware,” International Journal of Electronic Security and Digital Forensics, vol. 1, no. 2, pp. 156–168, 2007.

[22]      I. Santos, B. Sanz, C. Laorden, F. Brezo, P.G. Bringas, “Opcodesequence- based semi-supervised unknown malware detection”, Computational Intelligence in Security for Information Systems, pp. 50–57, Berlin, 2011.

[23]      F. Manavi, A. Hamzeh. "A new approach for malware detection based on evolutionary algorithm." In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1619-1624. ACM, 2019.

[24]      F. Manavi, A. Hamzeh, “A new method for malware detection using opcode visualization”, 2017 Artificial Intelligence and Signal Processing Conference (AISP), pp. 96-102, Shiraz, 2017.

[25]      M. Farrokhmanesh, A. Hamzeh, “A novel method for malware detection using audio signal processing techniques”, Artificial Intelligence and Robotics (IRANOPEN), pp. 85 – 91, 2016.

[26]      L. Nataraj, S. Karthikeyan, G. Jacob, B. S. Manjunath, “Malware images: Visualization and automatic classification,” In Proc. 8th Int. Symp. Visualization for Cyber Security, VizSec, ACM, pp. 41–47, 2011.

[27]      A. Oliva, A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” Int. J. Comput. Vision, vol. 42, pp. 145–175, 2001.

[28]      M. Douze, H. Jégou, H. Sandhawalia, L. Amsaleg, and C. Schmid, “Evaluation of gist descriptors for web-scale image search”, The ACM International Conference on Image and Video Retrieval pp. 19, ACM, 2009.

[29]      J. Hayes, A. Efros, “Scene completion using millions of photographs”, ACM Transactions on Graphics, 2007.

[30]      X. Li, C. Wu, C. Zach, S. Lazebnik, J.-M. Frahm, “Modeling and recognition of landmark image collections using iconic scene graphs”, In ECCV, 2008.

[31]      F. Lin, W.W. Cohen, “Power iteration clustering”, 27th International Conferenceon Machine Learning(ICML10), pp. 655–662, 2010.

[32]      M. Eskandari, Z. Khorshidpour, S. Hashemi, “HDM-analyser: a hybrid analysis approach based on data mining techniques for malware detection”, Journal of Computer Virology and Hacking Techniques, vol. 9, pp. 77–93, 2013.