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http://dx.doi.org/10.22156/CS4SMB.2018.8.6.165

Clasification of Cyber Attack Group using Scikit Learn and Cyber Treat Datasets  

Kim, Kyungshin (School of Mobile IT Tech, ChungKang College of Cultural Industries)
Lee, Hojun (School of Mobile IT Tech, ChungKang College of Cultural Industries)
Kim, Sunghee (DigitalTwin Com. Ltd)
Kim, Byungik (Dept. of Security Tech. R&D Team Korea Internet & Security Agency)
Na, Wonshik (Division of Computer Science, NamSeoul Univ.)
Kim, Donguk (Ncodi Com. Ltd)
Lee, Jeongwhan (AI Com. Ltd)
Publication Information
Journal of Convergence for Information Technology / v.8, no.6, 2018 , pp. 165-171 More about this Journal
Abstract
The most threatening attack that has become a hot topic of recent IT security is APT Attack.. So far, there is no way to respond to APT attacks except by using artificial intelligence techniques. Here, we have implemented a machine learning algorithm for analyzing cyber threat data using machine learning method, using a data set that collects cyber attack cases using Scikit Learn, a big data machine learning framework. The result showed an attack classification accuracy close to 70%. This result can be developed into the algorithm of the security control system in the future.
Keywords
Machine Learning; Sci-kit Learn; Cyber Treat; Cyber Attack Group; Cyber Attack Datasets;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 K. S. Kim. (2018). Malware Analysis Algorithm using Machine Learning. International Journal of Engineering & Technology, 7(2.12), 80-83.
2 T. K. Kwon. (2016). Maleware Various Group Classfy using Data Mining. Korea Internet & Security Agency Final Report.
3 E. K. Yang. (2010). Deveop of Performance Factor and Collect of Malware Analysis. Korea Internet & Security Agency Final Report.
4 J. S. Moon. (2010). Neutralization Algorithm Study using Execution Self-Compression file. Korea Internet & Security Agency Final Report.
5 B. I, Kim. (2018), A Study on Cyber Threat Intelligence Analysis (CTI) Platform for Proactive Detection of Cyber Attacks Based on Automated Analysis. The Journal of Korea Telecom Society, Fall Symposium, 578-579.
6 B. I, Kim. (2016), A Study on the ID Management System of Cyber Threat and its Relevant Information for Cyber Threat Intelligent Analysis. The Journal of Korea Telecom Society, Winter Symposium, 959-960.
7 Daesung Moon, Hansung Lee, (2014), "Feature Extraction for Host based Anomaly Detection", The Journal of Korea Electronics Society, Summer Symposium, 591-594
8 H. J. Kim & E. J. Yoon. (2017). AI Deep Learning protection of Malware Imagification. Journal of The Institute of Electronics and Information Engineers, 54(2).
9 Malware Images: Visualization and Automatic Classification, https://vision.ece.ucsb.edu/research/signal-processing-malware-analysis
10 S. H. Seok. (2016). Malware Family Classify of Convolution Neural Network using Imagification. Journal of the Korea Institute of Information Security & Cryptology, 26(1).
11 J. H. Kwon. (2011). Malware detection of Various code using Action Graph. Security of Information Society Journal, 21(2).
12 C. K. Kong. (2011). Malware Host Detection using Spam Mail Analysis. Korea Internet & Security Agency Final Report.
13 Splunk Product Bries. (2018). Splunk Enterprise Security. https://www.splunk.com/pdfs/product-briefs/splunk-enterprise-security.pdf
14 D. H. Kim & K. S. Kim. (2018). DGA-DNS Similarity Analysis and APT Attack Detection Using N-gram. The Journal of Korea Computer Secret Society, 28(5), 591-594.
15 D. G. Kim & C. H. Kim. (2018). Study on APT Attack Response Techniques Based on Big Data Analysis. The Journal of Society of Convergence Knowledge, 4(1), 29-34.