In this paper, we present to the community the first preliminary study on the security risks of Android TV apps. To the best of our knowledge, despite the fact that various efforts have been put into analyzing Android apps, our community has not explored TV versions of Android apps. There is hence no publicly available dataset containing Android TV apps. To this end, We start by collecting a large set of Android TV apps from the official Google Play store. We then experimentally look at those apps from four security aspects: VirusTotal scans, requested permissions, security flaws, and privacy leaks. Our experimental results reveal that, similar to that of Android smartphone apps, Android TV apps can also come with different security issues. We hence argue that our community should pay more attention to analyze Android TV apps.

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Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malware writers regularly update their attack mechanisms to hide malicious behavior implementation. This poses two problems to current research techniques: static analysis approaches, given their over-approximations, can report an overwhelming number of false alarms, while dynamic approaches will miss those behaviors that are hidden through evasion techniques. We propose in this work a static approach specifically targeted at highlighting hidden sensitive operations, mainly sensitive data flows. The prototype version of HiSenDroid has been evaluated on a large-scale dataset of thousands of malware and goodware samples on which it successfully revealed anti-analysis code snippets aiming at evading detection by dynamic analysis. We further experimentally show that, with FlowDroid, some of the hidden sensitive behaviors would eventually lead to private data leaks. Those leaks would have been hard to spot either manually among the large number of false positives reported by the state of the art static analyzers, or by dynamic tools. Overall, by putting the light on hidden sensitive operations, HiSenDroid helps security analysts in validating potential sensitive data operations, which would be previously unnoticed.

Objective Targeting the protein of interest to a particular tissue to achieve high-level expression is an important strategy to increase expression efficiency. The use of the plant seed oil body as a bioreactor can not only increase the amount of target protein, but also reduce the cost of downstream processing. Methods VEGF165 was expressed in Arabidopsis thaliana seeds via oilbody fusion technology. The pKO-VEGF165 vector was construted and transformed into A. thaliana seeds. T3 transgenic seeds was detected by SDS-PAGE and western blot methods. The cell activity was tested by MTT methods. Result The phaseolin promoter was used to drive seed-specific expression of the VEGF165 gene in transgenic A. thaliana. The coding region of VEGF165 was fused to the Arabidopsis oleosin sequence to target the protein to the oil bodies in the seeds of transgenic plants. The T-DNA region of recombinant plasmid pKO-VEGF165 was shifted to A. thaliana seeds via the floral-dip method. Protein was analyzed by electrophoresis and protein hybridization analyses. Finally, MTT assays showed that the oleosin-VEGF165 fusion protein played a part in the proliferation of HUVEC cells in vitro. Conclusion Oleosin-VEGF165 was successfully expressed and it had stimulated HUVEC cell proliferation activity.

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