한국멀티미디어학회(The Journal of Curriculum Evaluation)

In the field of information security, passwords are a means of authenticating users. Passwords with weak security cannot perform the role of user authentication and personal information protection because confidentiality is easily violated. To ensure confidentiality, it is important to evaluate the strength of the password and choose a very secure password. Due to this fact, security evaluation models for various passwords have been presented. However, existing evaluation models evaluate security based on the English alphabet. Passwords depend on the memory of the user and are closely related to the language or environment used by the user. In this regard, there are limitations in applying the existing security evaluation models to passwords chosen by non-English speakers. We compose a non-English, Korean language-based password dictionary and propose a password security evaluation model based on this for Korean users. In addition, to verify the effectiveness of the proposed model, we conducted experiments to evaluate the security of Korean language-based passwords using a database of passwords that have been actually leaked. As a result, the proposed model showed 99.38% accuracy for Korean language-based leaked passwords. This is superior to the 80.06% accuracy shown by the existing model. In conclusion, the use of the Korean language-based password security evaluation model proposed in this paper will contribute to choosing more secure passwords for Korean language-based sites or users.

It is very important to consider whether a password has been leaked, because security can no longer be guaranteed for passwords exposed to attackers. However, most existing password security evaluation methods do not consider the leakage of the password. Even if leakage is considered, a process of collecting, storing, and verifying a huge number of leaked passwords is required, which is not practical in low-performance devices such as IoT devices. Therefore, we propose another approach in this paper using a deep learning model. A password list was made for the proposed model by randomly extracting 133,447 words from a total of seven dictionaries, including Wikipedia and Korean-language dictionaries. After that, a deep learning model was created by using the three pieces of feature data that were extracted from the password list, as well as a label for the leakage. After creating an evaluation model in a lightweight file, it can be stored in a low-performance device and is suitable to predict and evaluate the security strength of a password in a device. To check the performance of the model, an accuracy evaluation experiment was conducted to predict the possibility of leakage. As a result, a prediction accuracy of 95.74% was verified for the proposed model.

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug–phenotype associations with therapeutic possibility. To validate the drug–phenotype associations predicted by our method, we make an enrichment comparison with known drug–phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

Coronary Heart Disease (CHD) is the world’s leading cause of death according to a World Health Organization (WHO) report. Despite the evolution of modern medical technology, the mortality rate of CHD has increased. Nevertheless, patients often do not realize they have CHD until their condition is serious due to the complexity, high cost, and the side effects of the diagnosis process. Thus, research on predicting CHD risk has been conducted. The Framingham study is a widely-accepted study in this field. However, one of its limitations is its overestimation of risk, which threatens its accuracy. Therefore, this study suggests a more advanced CHD risk prediction algorithm based on Optimized-DBN (Deep Belief Network). Optimized- DBN is an algorithm to improve performance by overcoming the limitations of the existing DBN. DBN does not have the global optimum values for number of layers and nodes, which affects research results. We overcame this limitation by combining with a genetic algorithm. The result of genetic algorithm for deriving the number of layers and nodes of Optimized-DBN for CHD prediction was 2 layers, 5 and 7 nodes to each layers. The accuracy of the CHD prediction algorithm based on Optimized- DBN which is developed by applying results of genetic algorithm was 0.8924, which is better than Framingham’s 0.5015 and DBN’s 0.7507. In the case of specificity, Optimized-DBN based CHD prediction was 0.7440, which was slightly lower than 0.8208 of existing DBN, but better than Framingham’s 0.65. In the case of sensitivity, Optimized-DBN is 0.8549, which is better than Framingham 0.4429 and DBN 0.7468. AUC of suggesting algorithm was 0.762, which was much better than Framingham 0.547 and DBN 0.570.