Browse > Article

머신러닝을 이용한 지능형 악성코드 분석기술 동향  

Lee, Taejin (호서대학교 컴퓨터정보공학부)
Keywords
Citations & Related Records
연도 인용수 순위
  • Reference
1 Baset, Mohamad. "MACHINE LEARNING FOR MALWARE DETECTION." (2016).
2 Yonts, Joel. "Attributes of malicious files." SANS Institute InfoSec Reading Room (2012).
3 Kabanga, Espoir K., and Chang Hoon Kim. "Malware Images Classification Using Convolutional Neural Network." Journal of Computer and Communications 6.01 (2017): 153.
4 Nataraj, Lakshmanan, et al. "Malware images: visualization and automatic classification." Proceedings of the 8th international symposium on visualization for cyber security. ACM, 2011.
5 Ahmadi, Mansour, et al. "Novel feature extraction, selection and fusion for effective malware family classification." Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy. ACM, 2016.
6 Jacob, Gregoire, et al. "A static, packer-agnostic filter to detect similar malware samples." International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, Berlin, Heidelberg, 2012.
7 Li, Yuping, et al. "Experimental study of fuzzy hashing in malware clustering analysis." 8th workshop on cyber security experimentation and test (cset 15). Vol. 5. No. 1. 2015.
8 You, Ilsun, and Kangbin Yim. "Malware obfuscation techniques: A brief survey." Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on. IEEE, 2010.
9 Liu, Liu, and Baosheng Wang. "Malware classification using gray-scale images and ensemble learning." Systems and Informatics (ICSAI), 2016 3rd International Conference on. IEEE, 2016.
10 Dahl, George E., et al. "Large-scale malware classification using random projections and neural networks." Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.
11 Souri, Alireza, and Rahil Hosseini. "A state-of-the-art survey of malware detection approaches using data mining techniques." Human-centric Computing and Information Sciences 8.1 (2018): 3.   DOI
12 Ucci, Daniele, Leonardo Aniello, and Roberto Baldoni. "Survey on the Usage of Machine Learning Techniques for Malware Analysis." arXiv preprint arXiv:1710.08189 (2017).
13 Saxe, Joshua, and Konstantin Berlin. "Deep neural network based malware detection using two dimensional binary program features." Malicious and Unwanted Software (MALWARE), 2015 10th International Conference on. IEEE, 2015.
14 Madry, Aleksander, et al. "Towards deep learning models resistant to adversarial attacks." arXiv preprint arXiv:1706.06083 (2017).
15 Lin, Chih-Ta, et al. "Feature Selection and Extraction for Malware Classification." J. Inf. Sci. Eng. 31.3 (2015): 965-992.
16 Ma hew Asquith. 2015. Extremely scalable storage and clustering of malware metadata. Journal of Computer Virology and Hacking Techniques (2015), 1-10.
17 Jinrong Bai, JunfengWang, and Guozhong Zou. 2014. A malware detection scheme based on mining format information. e Scienti c World Journal 2014 (2014
18 Mansour Ahmadi, Giorgio Giacinto, Dmitry Ulyanov, Stanislav Semenov, and Mikhail Tro mov. 2015. Novel feature extraction, selection and fusion for e ective malware family classi cation. CoRR abs/1511.04317 (2015).
19 Blake Anderson, Daniel ist, Joshua Neil, Curtis Storlie, and Terran Lane. 2011. Graph-based malware detection using dynamic analysis. Journal in Computer Virology 7, 4 (2011), 247-258.   DOI
20 Blake Anderson, Curtis Storlie, and Terran Lane. 2012. Improving malware classi cation: bridging the static/dynamic gap. In Proceedings of the 5th ACM workshop on Security and arti cial intelligence. ACM, 3-14.
21 Ki, Youngjoon, Eunjin Kim, and Huy Kang Kim. "A novel approach to detect malware based on API call sequence analysis." International Journal of Distributed Sensor Networks 11.6 (2015): 659101.   DOI
22 Ra qul Islam, Ronghua Tian, Lynn M Ba en, and Steve Versteeg. 2013. Classi cation of malware based on integrated static and dynamic features. Journal of Network and Computer Applications 36, 2 (2013), 646-656   DOI