Exploiting dynamic scheduling for VM-based code obfuscation

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program follows a single, static execution path for the same input. Such approaches, however, are vulnerable to certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications using similar protection schemes. This paper presents DSVMP, a novel VM-based code obfuscation approach for software protection. DSVMP brings together two techniques to provide stronger code protection than prior VM-based schemes. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution paths, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to perform attacks. Secondly, it employs multiple VMs to further obfuscate the relationship between VM bytecode and their interpreters, making code analysis even harder. We have implemented DSVMP in a prototype system and evaluated it using a set of widely used applications. Experimental results show that DSVMP provides stronger protection with comparable runtime overhead and code size when compared to two commercial VMbased code obfuscation tools

Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

The passive object localization (POL) problem in wireless sensor networks aims to determine the location of a target without any device attached for receiving or transmitting signal. This problem is challenging as there is very limited information available for deriving the target location. By combining the diffraction and scattering models, we propose a link sensing adaptive approach to POL, which first decides the target position attribute based on the signal strength and then localizes the target in different modes. We conduct rigorous localizability analyses and design a unit localization area scheme to achieve a higher level of localization accuracy. The efficacy of the proposed method is evaluated through comprehensive experiments in real life network environments

Ecological vulnerability assessment of a China's representative mining city based on hyperspectral remote sensing

Mining cities are clusters of communities that specialized in mining and extractive industries. The extensive mining activities in these cities have stimulated widespread and substantial ecological stresses to the surrounding environment, that significantly jeopardize the health condition of vegetation and human. Given the recent recognition of remote sensing in monitoring large-scale environmental change, we incorporated Ziyuan #1-02D, a recently released hyperspectral remote sensing data, into the ecological vulnerability assessment framework, using Panzhihua city as a case study, which is recognized as one of the most representative mining cities in China. The multi-spectral imaging data was widely applied in previous research. However, with the wide bands, multi-spectral imaging data cannot depict detailed characteristics of spectral targeted. As a result, we introduce indexes from the hyperspectral imaging to ecological vulnerability assessment proposed in this study, which can depict and monitor the growth and restoration of vegetation more accurately. We used the optimum index factor method to select bands from the satellite-based Ziyuan #1-02D data for quantifying vegetation indexes and red edge. Besides, we obtained inventory data, land-use, soil type, and typography of Panzhihua city to reconstruct its ecological vulnerability index (EVI) for 2020 and 2021. Comparing to the multi-spectral data, the ecological vulnerability results from hyperspectral imaging performed better in precision and concentration in EVI values, reaching the conclusions more directly. Specifically, the mining area and the relevant hazard types and impact areas were delineated through intensive fieldwork. Results suggested that the east and west districts, and north of Renhe district suffer great ecological stress, in which we observed intensive coal and metal-related mining industry. The central region, which occupies vanadium titanomagnetite mines, also shows substantial ecological issues, while the other mining industries, such as granite ore did not significantly influence the local environment. Although the newly released satellite-based data only have two-year periods, we still observed improving ecological conditions, with the southeast and west regions showing much lower ecological vulnerability values. The spatial autocorrelation analysis suggested that the high-high clustering region of EVI is located in the east, west, and Renhe districts, primarily due to the mining industries of variations scales. We also found that the clustering of low ecological vulnerable regions, mostly surrounds the vanadium titanomagnetite excavation industry, thanks to the local restoration projects