참고문헌
- Baset, Mohamad. "MACHINE LEARNING FOR MALWARE DETECTION." (2016).
- Yonts, Joel. "Attributes of malicious files." SANS Institute InfoSec Reading Room (2012).
- Kabanga, Espoir K., and Chang Hoon Kim. "Malware Images Classification Using Convolutional Neural Network." Journal of Computer and Communications 6.01 (2017): 153.
- Nataraj, Lakshmanan, et al. "Malware images: visualization and automatic classification." Proceedings of the 8th international symposium on visualization for cyber security. ACM, 2011.
- 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.
- 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.
- 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.
- You, Ilsun, and Kangbin Yim. "Malware obfuscation techniques: A brief survey." Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on. IEEE, 2010.
- 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.
- 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.
- 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. https://doi.org/10.1186/s13673-018-0125-x
- 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.
- Madry, Aleksander, et al. "Towards deep learning models resistant to adversarial attacks." arXiv preprint arXiv:1706.06083 (2017).
- Lin, Chih-Ta, et al. "Feature Selection and Extraction for Malware Classification." J. Inf. Sci. Eng. 31.3 (2015): 965-992.
- Ucci, Daniele, Leonardo Aniello, and Roberto Baldoni. "Survey on the Usage of Machine Learning Techniques for Malware Analysis." arXiv preprint arXiv:1710.08189 (2017).
- Ma hew Asquith. 2015. Extremely scalable storage and clustering of malware metadata. Journal of Computer Virology and Hacking Techniques (2015), 1-10.
- Jinrong Bai, JunfengWang, and Guozhong Zou. 2014. A malware detection scheme based on mining format information. e Scienti c World Journal 2014 (2014
- 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).
- 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. https://doi.org/10.1007/s11416-011-0152-x
- 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.
- 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 https://doi.org/10.1016/j.jnca.2012.10.004
- 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. https://doi.org/10.1155/2015/659101