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이태건(Taekeon Lee),김종성(Jongsung Kim),이창훈(Changhoon Lee),성재철(Jaechul Sung),이상진(Sangjin Lee) 한국정보보호학회 2006 정보보호학회논문지 Vol.16 No.1
일반적으로 블록암호를 이용한 암호화는 블록암호의 입력에 맞추기 위해 메시지의 마지막에 적당한 값을 패딩한다. 만약 공격자에게 평문의 패딩이 옳은지 아닌지의 여부를 판단하는 오라클이 있다면 임의의 암호문에 대응하는 평문을 찾는 공격을 패딩 오라클 공격이라 한다. 본 논문에서는 다양한 패딩 방법을 사용하는 블록암호의 이중 모드와 삼중 모드에 대해 이 공격을 수행한다. 이러한 안전성 분석 결과, 공격자가 패딩 오라클을 사용할 수 있다면 36개의 이중 모드들 중 12개, 216개의 삼중 모드들 중 22개가 패딩 오라클 공격에 취약함을 알 수 있었다. 이는 이중 모드와 삼중 모드들은 단얼 모드만큼의 안전성 밖에 제시하지 못한다는 사실을 나타낸다. This attack requires an oracle which on receipt of a ciphertext, decrypts it and replies to the sender whether the padding is VALID or INVALID. In this paper we extend these attacks to other kinds of modes of operation for block ciphers. Specifically, we apply the padding oracle attacks to multiple modes of operation with various padding schemes. As a results of this paper, 12 out of total 36 double modes and 22 out of total 216 triple modes are vulnerable to the padding oracle attacks. It means that the 12 double modes and the 22 triple modes exposed to these types of attacks do not offer the better security than single modes.
Target Prediction Based On PPI Network
Taekeon Lee(이태건),Youhyeon Hwang(황유현),Min Oh(오민),Youngmi Yoon(윤영미) 韓國컴퓨터情報學會 2016 韓國컴퓨터情報學會論文誌 Vol.21 No.3
To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer’s disease and 0.71 on Breast cancer.
서수경(Sukyung Seo),이태건(Taekeon Lee),윤영미(Youngmi Yoon) 한국정보기술학회 2019 한국정보기술학회논문지 Vol.17 No.12
Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.
문헌 기반 중요 유전자와 PPI 네트워크를 이용한 약물 재창출의 새로운 방법
박민석(Minseok Park),장기업(Giup Jang),이태건(Taekeon Lee),윤영미(Youngmi Yoon) 한국컴퓨터정보학회 2017 韓國컴퓨터情報學會論文誌 Vol.22 No.3
New drug development is time-consuming and costly. Hence, it is necessary to repurpose old drugs for finding new indication. We suggest the way that repurposing old drug using massive literature data and biological network. We supposed a disease-drug relationship can be available if signal pathways of the relationship include significant genes identified in literature data. This research is composed of three steps-identifying significant gene using co-occurrence in literature; analyzing the shortest path on biological network; and scoring a relationship with comparison between the significant genes and the shortest paths. Based on literatures, we identify significant genes based on the co-occurrence frequency between a gene and disease. With the network that include weight as possibility of interaction between genes, we use shortest paths on the network as signal pathways. We perform comparing genes that identified as significant gene and included on signal pathways, calculating the scores and then identifying the candidate drugs. With this processes, we show the drugs having new possibility of drug repurposing and the use of our method as the new method of drug repurposing.
유사 약물 예측을 위한 텍스트 마이닝 기반의 새로운 약물 유사도 측정 방법
장기업(Giup Jang),황유현(Youhyeon Hwang),오민(Min Oh),이태건(Taekeon Lee),윤영미(Youngmi Yoon) 한국정보기술학회 2016 한국정보기술학회논문지 Vol.14 No.7
There are many studies to identify new indications of existing drugs. Among them, text mining derives new relationships between drug and disease from vast unstructured data. In this study, co-occurrence of drug and disease in a sentences of abstracts in literatures is counted, and also co-occurrence of drug and gene is counted. Then drug-disease co-occurrence matrix and drug-gene co-occurrence matrix are generated. For each drug pairs using these matrices, disease-based drug similarity and gene-based drug similarity are calculated by mutual information. Also chemical, side-effect, and GO similarity are calculated for each drug pairs, respectively. Classification class label of “same” is given to each drug pair if ATC code for the two drugs of the pair is equal, and “different” is given to the pair otherwise. For the classification of drug pairs, AUC is improved with addition of mutual information, and we validated that text mining can help identifying similar drug. Identification of similar drugs can be utilized for drug repositioning.