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논문 기본 정보

자료유형
학술저널
저자정보
Minsoo Yeo (Kwangwoon University) Ilsub Bang (Sejong University) Donghyun Kim (Sejong University) Abbas Ahmad (Easy Global Market) Hamza Baqa (Easy Global Market) Jaeseung Song (Sejong University) Cheolsoo Park (Kwangwoon University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.7 No.4
발행연도
2018.8
수록면
313 - 320 (8page)
DOI
10.5573/IEIESPC.2018.7.4.313

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초록· 키워드

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The threat of malware in the Internet of Things (IoT) environment is increasing due to a lack of detectors. This paper proposes a method to predict the intrusion of malware using state-of-the- art machine learning algorithms that can detect malware faster and more accurately, compared with the existing methods (that is, payload, port-based, and statistical methods). A smart office environment was implemented to capture the flow of packet datasets, where malware and normal packets were captured, and 11 features were extracted from them. Four machine learning algorithms (random forest, a support vector machine, AdaBoost, and a Gaussian mixture model-based naïve Bayes classifier) were investigated to implement the automatic malware monitoring system. Random forest and AdaBoost could separate the malware and normal flows perfectly, due to their ensemble structures, which could classify unbalanced and noisy datasets.

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Abstract
1. Introduction
2. Background
3. Methods
4. Results
5. Conclusion & Discussion
References

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UCI(KEPA) : I410-ECN-0101-2018-569-003400901