• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.6
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• pp.1-9
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• 2016

In this study, we investigate the announcement effects of M&As with a start-up as a target firm on shareholder wealth of an acquiring firm. We use M&A events in KOSPI or KOSDAQ market between 2002 and 2014 after the financial crisis. Among the total 1436 mergers and acquisitions that took place domestically during this period, 1383 cases were selected as cases to be studied, excluding 53 cases where acquiring firms were unlisted firms. The results of the analysis are as follows: First, as a result of a comparison between the acquiring firms' CARs of the whole sample group(n=1383) occurred during the (-2, +1), (-5, +2), (-10, +5) periods of M&A announcement date(t=0) and the sub-sample group(n=468) where the target firms are start-ups which were established within five years, the acquiring firms of the whole sample group do not show significat CARs, while the acquiring firms of the sub-sample group show the significantly positive CARs. This suggests that M&A with start-ups have a positive effect on firm value of acquiring firms. Second, when merging unlisted start-ups, the acquiring firms show positive CARs, showing that there exists a listing effect in the merger of start-up. Third, merging the start-ups belonging to the high-tech industry shows the higher CARs than the case of merging the start-ups belonging to the non-high-tech industry. This study has great significance as the first in Korea to investigate the effect of M&A announcement with a start-up.