Self-Controllable Mobile App Protection Scheme Based on Binary Code Splitting

Mobile apps are booming with the expansion of mobile devices such as smartphones, tablet PCs, smartwatches, and IoT devices. As the capabilities of mobile apps and the types of personal information required to run apps have diversified, the need for increased security has grown. In particular, Android apps are vulnerable to repackaging attacks, so various code protection techniques such as obfuscation and packing have been applied. However, apps protected with these techniques can also be disabled with static and dynamic analyses. In recent years, instead of using such application level protection techniques, a number of approaches have been adopted to monitor the behavior of apps at the platform level. However, in these cases, not only incompatibility of system software due to platform modification, but also self-control functionality cannot be provided at the user level and is very inconvenient. Therefore, in this paper we propose an app protection scheme that can split a part of the app code, store it in a separate IoT device, and self-control the split code through the partial app. In the proposed scheme, the partial app is executed only when it matches the split code stored in the IoT device. It does not require complicated encryption techniques to protect the code like the existing schemes. It also provides solutions to the parameter dependency and register reallocation issues that must be considered when implementing the proposed code splitting scheme. Finally, we present and analyze the results of experimenting the proposed scheme on real devices.

Multi-parameter calibration of a UV/Vis spectrometer for online monitoring of sewer systems

UV/Vis spectrometers are powerful tools for online monitoring of wastewater constituents and processes. However, most studies only focus on typical parameters such as chemical oxygen demand (COD) and total suspended solids. This work presents a multi-parameter approach for calibration of a UV/Vis spectrometer for online monitoring of sewer systems. Parameters studied include soluble and total COD, nitrate, ammonium, sulphate and orthophosphate, as well as total dissolved sulphide, bisulphide and hydrogen sulphide, because they are one of the main causes for odour and corrosion in sewer systems. Two calibration methods are compared: multiple linear regression included in the manufacturer's software, and partial least square (PLS) computed using the pls package of the R library. Performance of the methods is evaluated for calibration and validation data sets employing four different criteria: relative root mean square error (RMSErel), RMSE-observations standard deviation ratio, Nash–Sutcliffe efficiency and percentage bias. A method-parameter dependency was revealed during the calibration phase but, when predicting new data, the PLS method showed higher robustness for almost all parameters. Both methods were able to predict concentration trends associated with sewer processes, some of which are strongly correlated to the sulphide species.