Sung-Sam Hong, Dong-Wook Kim, Byungik Kim, Myung-Mook Han, Journal of Internet Computing and Services, Vol. 18, No. 1, pp. 65-76, Feb. 2017
10.7472/jksii.2017.18.1.65, Full Text:  HTML
Keywords: Labeling, Detection Model based on classification, Data Mining, Inference, Supervised/Unsupervised Learning, Security

Abstract

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

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