Ubiquitous Health Management System with Watch-Type Monitoring Device for Dementia Patients

For patients who have a senile mental disorder such as dementia, the quantity of exercise and amount of sunlight are an important clue for doses and treatment. Therefore, monitoring daily health information is necessary for patients’ safety and health. A portable and wearable sensor device and server configuration for monitoring data are needed to provide these services for patients. A watch-type device (smart watch) that patients wear and a server system are developed in this paper. The smart watch developed includes a GPS, accelerometer, and illumination sensor, and can obtain real time health information by measuring the position of patients, quantity of exercise, and amount of sunlight. The server system includes the sensor data analysis algorithm and web server used by the doctor and protector to monitor the sensor data acquired from the smart watch. The proposed data analysis algorithm acquires the exercise information and detects the step count in patients’ motion acquired from the acceleration sensor and verifies the three cases of fast pace, slow pace, and walking pace, showing 96% of the experimental results. If developed and the u-Healthcare System for dementia patients is applied, higher quality medical services can be provided to patients

Modulation of Bax and mTOR for Cancer Therapeutics.

A rationale exists for pharmacologic manipulation of the serine (S)184 phosphorylation site of the proapoptotic Bcl2 family member Bax as an anticancer strategy. Here, we report the refinement of the Bax agonist SMBA1 to generate CYD-2-11, which has characteristics of a suitable clinical lead compound. CYD-2-11 targeted the structural pocket proximal to S184 in the C-terminal region of Bax, directly activating its proapoptotic activity by inducing a conformational change enabling formation of Bax homooligomers in mitochondrial membranes. In murine models of small-cell and non-small cell lung cancers, including patient-derived xenograft and the genetically engineered mutant KRAS-driven lung cancer models, CYD-2-11 suppressed malignant growth without evident significant toxicity to normal tissues. In lung cancer patients treated with mTOR inhibitor RAD001, we observed enhanced S184 Bax phosphorylation in lung cancer cells and tissues that inactivates the propaoptotic function of Bax, contributing to rapalog resistance. Combined treatment of CYD-2-11 and RAD001 in murine lung cancer models displayed strong synergistic activity and overcame rapalog resistanc

BGP Dataset-Based Malicious User Activity Detection Using Machine Learning

Recent advances in the Internet and digital technology have brought a wide variety of activities into cyberspace, but they have also brought a surge in cyberattacks, making it more important than ever to detect and prevent cyberattacks. In this study, a method is proposed to detect anomalies in cyberspace by consolidating BGP (Border Gateway Protocol) data into numerical data that can be trained by machine learning (ML) through a tokenizer. BGP data comprise a mix of numeric and textual data, making it challenging for ML models to learn. To convert the data into a numerical format, a tokenizer, a preprocessing technique from Natural Language Processing (NLP), was employed. This process goes beyond merely replacing letters with numbers; its objective is to preserve the patterns and characteristics of the data. The Synthetic Minority Over-sampling Technique (SMOTE) was subsequently applied to address the issue of imbalanced data. Anomaly detection experiments were conducted on the model using various ML algorithms such as One-Class Support Vector Machine (One-SVM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), Random Forest (RF), and Autoencoder (AE), and excellent performance in detection was demonstrated. In experiments, it performed best with the AE model, with an F1-Score of 0.99. In terms of the Area Under the Receiver Operating Characteristic (AUROC) curve, good performance was achieved by all ML models, with an average of over 90%. Improved cybersecurity is expected to be contributed by this research, as it enables the detection and monitoring of cyber anomalies from malicious users through BGP data

Mission-Based Cybersecurity Test and Evaluation of Weapon Systems in Association with Risk Management Framework

With the advancement of information technology (IT), the importance of cyber security is increasing because of the expansion of software utilization in the development of weapon systems. Civilian embedded systems and military weapon systems have cybersecurity-related symmetry that can increase vulnerabilities in the process of advanced information technology. Many countries, including the United States, are exploring ways to improve cybersecurity throughout the lifecycle of a weapon system. The South Korean military is applying the U.S. standard risk management framework (RMF) to some weapon systems to improve cybersecurity, but the need for a model that is more suitable for the South Korean military has been emphasized. This paper presents the results of a mission-based cybersecurity test, along with an evaluation model that can be applied to South Korean military weapon systems in parallel with the RMF. This study first examined the related international research trends, and proposed a test and evaluation method that could be utilized with the RMF throughout the entire life cycle of a weapon system. The weapon system was divided into asset, function, operational task, and mission layers based on the mission, and a mutually complementary model was proposed by linking the RMF and cybersecurity test and evaluation according to the domestic situation. In order to verify the proposed cybersecurity test and evaluation model, a simulation was developed and performed targeting the Close Air Support (CAS) mission support system, which is a virtual weapon system. In this simulation, the nodes performances by layer before and after a cyberattack were calculated, and the vulnerabilities and protection measures identified in the cyber security test and evaluation were quantified. This simulation made it possible to evaluate and derive protection measures in consideration of mission performance. It is believed that the proposed model could be used with some modifications, depending on the circumstances of each country developing weapon systems in the future

High capacity data hiding with absolute moment block truncation coding image based on interpolation

Data hiding is a way of hiding secret data on cover-media and it is used for a variety of applications. An important of the data hiding is to conceal the data in a secret way without loss of cover-media. Until now, continuous research on absolute moment block truncation coding based data hiding methods have improved a performance on data concealment and image quality. However, the current absolute moment block truncation coding based data hiding technology has a limitation in deriving a method that significantly surpasses existing performance. In this paper, we propose a new method to overcome this problem. To do this, first the original image is transformed to the cover image using absolute moment block truncation coding and is expanded the image using neighbor average interpolation algorithm. The proposed three data hiding methods are based on the generated cover image. The first method is to directly replace the pixel value, which is a component of each block, with the same secret value. The second method is to replace the pixels to match the secret bits only for the extended pixels in each block of the cover image. The third method is to apply Hamming code to each block to minimize the number of replacement pixels for data hiding. Experimental results show that our method is superior in terms of efficiency compared to traditional absolute moment block truncation coding based data hiding methods