• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1350-1359
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• 2010

Recently many researchers have been proved that general RFID system for proximity authentication is vulnerable to various location-based relay attacks such as distance fraud, mafia fraud and terrorist fraud attacks. The distance-bounding protocol is used to prevent the relay attacks by measuring the round trip time of single challenge-response bit. In 2008, Munilla and Peinado proposed an improved distance-bounding protocol applying void-challenge technique based on Hancke-Kuhn's protocol. Compare with Hancke-Kuhn's protocol, Munilla and Peinado's protocol is more secure because the success probability of an adversary has (5/8)n. However, Munilla and Peinado's protocol is inefficient for low-cost passive RFID tags because it requires large storage space and many hash function computations. Thus, this paper proposes a new RFID distance-bounding protocol for low-cost passive RFID tags that can be reduced the storage space and hash function computations. As a result, the proposed distance-bounding protocol not only can provide both storage space efficiency and computational efficiency, but also can provide strong security against the relay attacks because the adversary's success probability can be reduced by $(5/8)^n$.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.

• 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 Chest Surgery
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• v.32 no.9
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• pp.790-798
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• 1999

Background: As the early outcome after coronary artery bypass grafting(CABG) has been stabilized, neurologic complication has now become one of the most important morbidity. The aim of this study was to find out the risk factors associated with the neurologic complications after CABG. Material and Method: In 351 patients who underwent CABG, the incidence and features of neurologic complications, with associated perioperative risk factors, were retrospectively reviewed. Neurologic complication was defined as a new cerebral infarction confirmed by postoperative neurologic examination and radiologic studies, or delayed recovery of consciousness and orientation for more than 24 hours after the operation. Result: Neurologic complications occurred in 18 patients(5.1%), of these nine(2.6%) were diagnosed as having new cerebral infarctions(stroke). Stroke was manifested as motor paralysis in four patients, mental retardation or orientation abnormality in four, and brain death in one. Statistical analysis revealed the following variables as significant risk factors for neurologic complications by both univariate and multivariate analyses: cardiopulmonary bypass longer than 180 minutes, atheroma of the ascending aorta, carotid artery stenosis detected by Duplex sonography, and past history of cerebrovascular accident or transient ischemic attack. Age over 65 years, aortic calcification detected by simple X-ray, and intraoperative myocardial infarction were significant risk factors by univariate analysis only. Neither the severity of carotid artery stenosis nor technical modifications such as cannulation of the aortic arch or single clamp technique, which were expected to affect the inciden e of neurologic complications, had significant relationship with the incidence. Conclusion: This study confirmed the strong association between neurologic complications after CABG and atherosclerosis of the arterial system. Therefore, to minimize the incidence of neurologic complications, systematic evaluation focused on atherosclerotic lesions of the arterial system followed by adequate alteration of operative strategy is needed.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (