A Study on the Establishment of the IDS Using Machine Learning

Kang Hyun Sun

Journal of Software Assessment and Valuation

Abbr : JSAV
2019, 15(2), pp.121-128
Publisher : Korea Software Assessment and Valuation Society
Research Area : Computer Science

Kang Hyun Sun ¹

¹남서울대학교

ABSTRACT

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.
KEYWORDS

IDS, machine learning, cyber attack, big data, security
REFERENCES (10)

[web] Korea Internet & Security Agency / 2019 / Cyber Security Threat Trend Report / https://www.boho.or.kr

[book] Shai Ben-David / 2014 / Understanding Machine Learning: From Theory to Algorithms / Cambridge University Press

[book] Andreas C. Müller / 2016 / Introduction to Machine Learning with Python: A Guide for Data Scientists / O'Reilly Media, Inc.

[web] Giuseppe Bonaccorso / 2007 / Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning / https://dl.acm.org/citation.cfm?id=3165154

[journal] Yoshua Bengio / 2004 / No Unbiased Estimator of the Variance of K-Fold Cross-Validation / The Journal of Machine Learning Research 5 : 1089~1105

[journal] Leo Breiman / 1996 / Heuristics of Instability and Stabilization in Model Selection / The Annals of Statistics 24(6) : 2350~2383 / https://projecteuclid.org/download/pdf_1/euclid.aos/1032181158

[journal] Ji-Hyun Kim / 2009 / Estimating Classification Error Rate: Repeated Cross-Validation, Repeated Hold-out and Bootstrap / Computational Statistics & Data Analysis 53 : 3735~3745

[web] Gartner, Analyst / 2017 / Preparing and Architecting for Machine Learning / https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf

[journal] Hawkins, Douglas M. / 2003 / Assessing Model Fit by Cross-Validation / Journal of Chemical Information and Computer Sciences : 579~586

[confproc] Kohavi, Ron / 1995 / A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection / International Joint Conference on Artificial Intelligence : 1137~1143
KCI Citation (1)

KJC Korea
Journal Central

Statistics

Tools