A Study on Variant Malware Detection Techniques Using Static and Dynamic Features
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signaturebased malware detection method. Thus, research on automated methods ofdetecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malwareclassification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy
Abstract
The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signaturebased malware detection method. Thus, research on automated methods ofdetecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malwareclassification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy
참고문헌 (20)
- "AV-TEST Annual Report" (2018)[URL]
- H. Rathore, "Big Data Analytics", pp.402~411 (2018), Springer
- R. Zak, "What can N-grams learn for malware detection?", Proceedings of 201712th International Conference on Malicious and Unwanted Software(MALWARE), pp.109~118 (2017)
- I. Santos, "Opcode sequences as representation of executables for data-mining-based unknown malware detection", Information Sciences, 231, pp.64~82 (2013)
- R. Moskovitch, "Intelligence and Security Informatics", pp.204~215 (2018), Springer
- A. Yewale, "Malware detection based on opcode frequency", Proceedings of 2016International Conference on Advanced Communication Control and Computing Technologies(ICACCCT), pp.646~649 (2016)
- Y. Ki, "A novel approach to detect malware based on API call sequence analysis", International Journal of Distributed Sensor Networks, 11(6) (2015)
- M. A. Jerlin, "A new malware detection system using machine learning techniques for API call sequences", Journal of Applied Security Research, 13(1), pp.45~62 (2018)
- M. Ahmadi, "Novel feature extraction, selection and fusion for effective malware family classification", Proceedings of the 6th ACM Conference on Data and Application Security and Privacy, pp.183~194 (2016)
- K. Rieck, "Detection of Intrusions and Malware, and Vulnerability Assessment", pp.108~125 (2008), Springer
- A. Mohaisen, "AMAL : high-fidelity, behavior-based automated malware analysis and classification", Computers & Security, 52, pp.251~266 (2015)
- S. Nari, "Automated malware classification based on network behavior", Proceedings of 2013 International Conference on Computing, pp.642~647 (2013)
- J. Saxe, "Deep neural network based malware detection using two dimensional binary program features", Proceedings of 2015 10th International Conference on Malicious and Unwanted Software(MALWARE), pp.11~20 (2015)
- X. Ma, "How to make attention mechanisms more practical in malware classification", IEEE Access, 7, pp.155270~155280 (2019)
- Z. Cui, "Detection of malicious code variants based on deep learning", IEEE Transactions on Industrial Informatics, 14(7), pp.3187~3196 (2018)
- D. Gibert, "Using convolutional neural networks for classification of malware represented as images", Journal of Computer Virology and Hacking Techniques, 15(1), pp.15~28 (2019)
- L. Nataraj, "Malware images : visualization and automatic classification", Proceedings of the 8th international Symposium on Visualization for Cyber Security, pp.1~7 (2011)
- "Deep transfer learning for static malware classification" (2018)[URL]
- S. Gupta, "Security, Privacy, and Applied Cryptography Engineering", pp.271~280 (2016), Springer
- "Large malware dataset" (2018)[URL]