• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korean Wood Science and Technology
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• v.36 no.6
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• pp.77-87
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• 2008

Wooden model erosion control dam was made with pitch pine, of which the strength properties was evaluated. Wooden model erosion control dam was made with diameter 90 mm of pitch pine round posts treated with CUAZ-2 (Copper Azole), changing joint in three different types. In each type, erosion control dam was made in nine floor (cross-bar of five floors and vertical-bar of four floors), of which the hight was 790 mm. And then strength properties were investigated through horizontal loading test and impact strength test, and the deformation of structure through image processing (AICON 3D DPA-PRO system). In horizontal loading test of wooden model erosion control dam using round post of diameter 90 mm, whether there was stone or not did not affect strength much when using self drill screw, but strength was decreased by 23%. In monolithic type of erosion control dam using screw bar, strength was increased by 1.5 times and deformation was decreased when filling with stone. When reinforcing with screw bar that ring is connected to self drill screw, strength was increased by 4.8 times. In impact strength test of wooden model erosion control dam made with round post of diameter 90 mm, the erosion control dam connected with self drilling screw not filling with stone was totally destroyed by the 1st impact, and the erosion control dam using screw bar was ruptured at cross-bar at which 779 kgf of impact was loaded in the 1st impact. In the 2nd impact, the base parts were ruptured, and reaction force was decreased to 545 kgf. In the 3rd impact, whole base parts were destroyed, and reaction force was decreased to 263 kgf.