• Journal of Digital Convergence
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• v.19 no.9
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• pp.463-468
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• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.785-794
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• 2019

Recently Deep Learning technology, one of the fourth industrial revolution technologies, is used to identify the hidden meaning of network data that is difficult to detect in the security arena and to predict attacks. Property and quality analysis of data sources are required before selecting the deep learning algorithm to be used for intrusion detection. This is because it affects the detection method depending on the contamination of the data used for learning. Therefore, the characteristics of the data should be identified and the characteristics selected. In this paper, the characteristics of malware were analyzed using network data set and the effect of each feature on performance was analyzed when the deep learning model was applied. The traffic classification experiment was conducted on the comparison of characteristics according to network characteristics and 96.52% accuracy was classified based on the selected characteristics.

• KNOM Review
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• v.22 no.1
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• pp.42-51
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• 2019

Recently, the amount of internet traffic is increasing due to the increase in speed and capacity of the network environment, and protocol data is increasing due to mobile, IoT, application, and malicious behavior. Most of these private protocols are unknown in structure. For efficient network management and security, analysis of the structure of private protocols must be performed. Many protocol reverse engineering methodologies have been proposed for this purpose, but there are disadvantages to applying them. In this paper, we propose a methodology for inferring a detailed protocol structure based on network trace analysis by hierarchically combining CSP (Contiguous Sequential Pattern) and SP (Sequential Pattern) Algorithm. The proposed methodology is designed and implemented in a way that improves the preceeding study, A²PRE, We describe performance index for comparing methodologies and demonstrate the superiority of the proposed methodology through the example of HTTP, DNS protocol.