Big Data Framework for Zero-Day Malware Detection
Author:
Affiliation:
1. Department of Computer Science and Engineering, Thapar University, Patiala, Punjab, India
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Information Systems,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/01969722.2018.1429835
Reference17 articles.
1. Ahn, S.H., N.U. Kim, and T.M. Chung. 2014. Big data analysis system concept for detecting unknown attacks. Advanced communication technology (ICACT), 2014 16th International Conference on, IEEE, Pyeongchang, South Korea, 269–72.
2. Anderson, B., C. Storlie, and T. Lane. 2012. Improving malware classification: Bridging the static/dynamic gap. Proceedings of the 5th ACM workshop on Security and artificial intelligence, ACM, Raleigh, North Carolina, USA, 3–14.
3. A comparison of static, dynamic, and hybrid analysis for malware detection
4. Gandotra, E., D. Bansal, and S. Sofat. 2014. Integrated framework for classification of malwares. Proceedings of the 7th International Conference on Security of Information and Networks, ACM, Glasgow, Scotland, UK, 417.
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