2020, Vol.6, No.2

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

pISSN : 2466-0078
https://journal.kci.go.kr/kiots

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

Since the end devices of the Internet of Things (IoT) are battery operated products, careful consideration for ultra-low power (ULP) is required. The Micro Controller Unit (MCU) industry has developed very effective functions to save energy, but developers have difficulty in selecting the MCU because various operating modes are applied to reduce energy consumption by manufacturers. Therefore, this paper introduces ULPMark benchmark, a standardized benchmark method that can compare MCUs of various vendors and feature sets, and provides hardware functions for ultra-low-power operation of the two platforms that received the high evaluation scores from ULPMark. In addition, we investigated and analyzed how developers can utilize the functions for ultra low power consumption through driver APIs and detailed register control.

In the present study, using biological information of bacteria and biochemical information of chlorine dioxide gas, Gram-positive bacteria, e.g., Alloiococcus otitis, Erysipelothrix rhusiopathiae, Staphylococcus caprae, Staphylococcus lentus, and gram-negative bacteria, e.g., Acinetobacter baumannii complex, Aeromonas salmonicida, Brucella melitensis, Oligella ureolytica were used whether a plastic kit to release ClO2 gas could inhibit their growth. Overall, chlorine dioxide gas showed about 99% inhibition of bacterial growth, with less than 10 CFU. However, it was found that Gram positive Alloiococcus otitis and Gram negative Aeromonas salmonicida had more than about 50 CFU. When comparing the results of experiments with several bacteria, it suggested that the concentration of chlorine dioxide gas would be at least 10 ppm to 400 ppm for the bacterial inhibition. The results of this study could be used as basic data to evaluate the clinical usefulness of chlorine dioxide gas. If this study helps with prior knowledge to help clinicians to recognize and prevent the presence of micro-organisms that cause infections in hospitals, it would be helpful for activities such as patient care as a convergence field. In the future, it is considered that the research results will be the basis for rapidly inhibiting the microbes infected with patients by utilizing data of the information of the microbes that are inhibited for chlorine dioxide gas.