Mobile applications (or apps) are rapidly growing in number and variety. These apps provide useful features, but also bring certain privacy and security risks. For example, malicious authors may attach destructive payloads to legitimate apps to create so-called "piggybacked" apps and advertise them in various app markets to infect unsuspecting users. To detect them, existing approaches typically employ pair-wise comparison, which unfortunately has limited scalability. In this paper, we present a fast and scalable approach to detect these apps in existing Android markets. Based on the fact that the attached payload is not an integral part of a given app's primary functionality, we propose a module decoupling technique to partition an app's code into primary and non-primary modules. Also, noticing that piggybacked apps share the same primary modules as the original apps, we develop a feature fingerprint technique to extract various semantic features (from primary modules) and convert them into feature vectors. We then construct a metric space and propose a linearithmic search algorithm (with O(n log n) time complexity) to efficiently and scalably detect piggybacked apps. We have implemented a prototype and used it to study 84, 767 apps collected from various Android markets in 2011. Our results show that the processing of these apps takes less than nine hours on a single machine. In addition, among these markets, piggybacked apps range from 0.97% to 2.7% (the official Android Market has 1%). Further investigation shows that they are mainly used to steal ad revenue from the original developers and implant malicious payloads (e.g., for remote bot control). These results demonstrate the effectiveness and scalability of our approach.
Device-to-Device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based singlechannel networks or aimed to maximize network throughput. In this paper, we, however, aim at enabling green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize power consumption while meeting user data rate requirements. We then present an effective algorithm to solve it in polynomial time, which jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithm can achieve over 57% power savings, compared to several baseline methods.
With the widespread popularity of Internet-enabled devices, mobile users can request and receive messages anytime and anywhere, which facilitates information feedback for smart city management. However, few people are willing to reflect or report some violations of law and discipline around them, and more people choose to ignore. In general, there are two major reasons for this phenomenon. First, reporting with a real name is highly recommended, but it is difficult to send trusted and reliable reporting messages without revealing the reporter's identity. Second, generally no benefit, users usually lack the motivation to report due to worrying about being retaliated. In this paper, we propose an effective anonymous reporting system called ReportCoin, a novel Blockchain-based incentive anonymous reporting system. ReportCoin guarantees user identity privacy and reporting message reliability throughout the reporting process. On the one hand, ReportCoin allows nondeterministic mobile users to vote the reporting by signing and to send anonymous announcements in the non-fully trusted network. On the other hand, ReportCoin motivates users with incentives to report without worrying about the disclosure of identity information to be retaliated. Meanwhile, account information and transaction records in ReportCoin are open, transparent, and tamperresistant. The theoretical analysis and extensive experimental results show that ReportCoin is efficient and practical.
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