Browse > Article
http://dx.doi.org/10.22156/CS4SMB.2019.9.3.134

Intrusion Situation Classification Model for Intelligent Intrusion Awareness  

Hwang, Yoon-Cheol (Department of Talmage Liberal Arts College, Hannam University)
Mun, Hyung-Jin (Department of Information & Communication Engineering, Sungkyul University)
Publication Information
Journal of Convergence for Information Technology / v.9, no.3, 2019 , pp. 134-139 More about this Journal
Abstract
As the development of modern society progresses rapidly, the technologies of society as a whole are progressing and becoming more advanced. Especially in the field of security, more sophisticated and intelligent attacks are being created. Meanwhile, damaging situations are becoming several times larger than before Therefore, it is necessary to re-classify and enhance the existing classification system. It is required to minimize the intrusion damage by actively responding to intelligent intrusions by applying this classification scheme to currently operating intrusion detection systems. In this paper, we analyze the intrusion type caused by intelligent attack We propose a new classification scheme for intrusion situations to guarantee the service safety, reliability, and availability of the target system, We use this classification model to lay the foundations for the design and implementation of a smart intrusion cognitive system capable of early detection of intrusion, the damages caused by intrusion, and more collections active response.
Keywords
Intelligent Intrusion; Intrusion Classification; Active response; Intrustion Situation; Intrusion Awareness System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 H. J. Seo, D. G. Lee, J. S. Choi & H. W. Kim.(2013). IoT Security Technology Trend. Journal of KIEES, 24(4), 27-35.
2 M. Abomhara. (2015). Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks. Journal of Cyber Security, 4, 65-88.   DOI
3 H. H. Lee, Y. Y. Lee & J. S. An. (2016). Commercial and Public Software Intentional Security Weakness Trend. Journal of Information Security, 26(1), 9-19.
4 W. S. Choi, H. S. Kim & D. H. Lee. (2018). Cryptojacking Research Trends. Journal of Information Security, 28(3), 33-37.
5 Symantec. (2017). 2017 Internet Security Threat Report. 22.https://www.symantec.com/content/dam/symantec/doc s/reports/istr-22-2017-en.pdf
6 Y. X. Meng. (2011). The practice on using machine learning for network anomaly intrusion detection. In Machine Learning and Cybernetics (ICMLC), 2011 International Conference on, 2(1), 576-581.
7 H. J. Mun. (2018). Biometric Information and OTP based on Authentication Mechanism using Blockchain. Journal of Convergence for Information Technology, 8(3), 85-90.   DOI
8 H. J. Mun, Y. C. Hwang & H. Y. Kim. (2015). Countermeasure for Prevention and Detection against Attacks to SMB Information System - A Survey. Journal of Convergence for Information Technology, 5(2), 1-6.   DOI
9 K. S. Kim & M. S. Kang. (2014). Next Generation Cyber Security Issues, Threats and Countermeasures. Journal of Electrical Engineering, 41(4), 69-77.
10 AhnLab. (2018). security threat trend.
11 C. T. Lim, J. H. Oh & H. C. Jung. (2010). Trend of Malicious Code Technology and Analysis Method. Information Science Society, 28(11), 117-126.
12 M. S Gu & Y. Z. Lee. (2015). A Study of Countermeasures for Advanced Persistent Threats attacks by malicious code. Journal of Convergence for Information Technology, 5(4), 37-42.   DOI
13 H. J. Mun, S. H. Choi & Y. C. Hwang. (2016). Effective Countermeasure to APT Attacks using Big Data. Journal of Convergence for Information Technology, 6(1), 17-23.   DOI
14 R. Von Solms & J. Van Niekerk. (2013). From information security to cyber security. computers & security, 38, 97-102.   DOI
15 K. Panetta. (2017). 5 Trends in Cybersecurity for 2017 and 2018. Smarter with Gartner.