과제정보
This work was supported by 2021 Hannam University Research Fund.
참고문헌
- AVTEST. (2021). https://www.av-test.org/en/statistics/malware/.
- Symantec, Symantec internet security threat report. (2018). ISTR-23-2018
- Chionis, I., Nikolopoulos, S. D. & Polenakis I. (2013). A Survey on Algorithmic Techniques for Malware Detection. Proc. 2nd Int'l Symposium on Computing in Informatics and Mathematics (ISCIM'13). 29-34.
- Xu, X. & Wang, X. (2005). An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. Advanced Data Mining and Applications, Springer, 696-703.
- M. Egele, T. S. Scholte, E. Kirda & C. Kruegel. (2012). A survey on automated dynamic malware-analysis techniques and tools. ACM Computing Surveys(CSUR). 44(2), 1-42.
- H. J. Gwon, S. W. Kim & E. G. Lim. (2012). An Malware Classification System using Multi N-gram. Journal of Security Engineering, 9(6), 531-542.
- H. S. Seo. J. S. Choi & P. H. Chu. (2009). Design of Classification Methodology of Malicious Code in Windows Environment. Journal of the Korea Institute of Information Security & Cryptology 19(2). 83-92. DOI: 10.13089/JKIISC.2009.19.2.83
- Cyber Threat Trend Report. (2021). KISA. https://www.krcert.or.kr/data/reportView.do?bulletin_writing_sequence=36189
- Cyber Threat Trend Report. (2021). KISA. https://www.krcert.or.kr/data/reportView.do?bulletin_writing_sequence=36076
- M. Sikorski & A. Honig. (2012). Practical Malware Analysis: the hands-on guide to dissecting malicious software. No Starch Press.
- Chionis, I. Nikolopoulos, S. D. & Polenakis I. (2013). A Survey on Algorithmic Techniques for Malware Detection. Proc. 2nd Int'l Symposium on Computing in Informatics and Mathematics (ISCIM'13). 29-34.
- W. K. Lee, M. J. Lee & D. S. Seo. (2020). Application of Machine Learning Techniques for the Classification of Source Code Vulnerability. Journal of The Korea institute of imformation security & cryptology, 30(4), 735-743.
- Sihwail, R., Omar, K. & Ariffin, K. Z. (2018). A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis. Int. J. Adv. Sci. Eng. Inf. Technol, 8(4-2), 1662-1671. DOI: 10.18517/ijaseit.8.4-2.6827
- K. Rieck, T. Holz, C. Willems, P. Dussel & P. Laskov. (2008). Leaming and classification of malware behavior. in Proceedings of the 5th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, 108-125.
- NSL-KDD dataset [online] available: http://nsl.cs.unb.ca/nsl-kdd/
- VX Heaven. (2018). http://83.133.184.251/virensimulation.org
- Wang, W., Zhang, X. & Gombault, S. (2009). Constructing Attribute Weights from Computer Audit Data for Effective Intrusion Detection. Journal of Systems and Software, 82, 1974-1981. https://doi.org/10.1016/j.jss.2009.06.040
- S. Cateni, et al. (2012). Variable Selection and Feature Extraction through Artificial Intelligence Techniques, Multivariate Analysis in Management. Engineering and the Science, chapter 6, 103-118.
- Makandar, A. & Patrot, A. (2015). Malware analysis and classification using artificial neural network. IEEE. In 2015 International conference on trends in automation, communications and computing technology (I-TACT-15). 1-6.
- P. Manandhar. (2014). A Practical Approach to Anomaly-based Intrusion Detection System by Outlier Mining in Network Traffic. Masdar Institute of Science and Technology.