Repackaged Android applications (app clones) have been found in many third-party markets, which not only compromise the copyright of original authors, but also pose threats to security and privacy of mobile users. Both fine-grained and coarse-grained approaches have been proposed to detect app clones. However, fine-grained techniques employing complicated clone detection algorithms are difficult to scale to hundreds of thousands of apps, while coarse-grained techniques based on simple features are scalable but less accurate. This paper proposes WuKong, a two-phase detection approach that includes a coarse-grained detection phase to identify suspicious apps by comparing light-weight static semantic features, and a fine-grained phase to compare more detailed features for only those apps found in the first phase. To further improve the detection speed and accuracy, we also introduce an automated clustering-based preprocessing step to filter third-party libraries before conducting app clone detection. Experiments on more than 100,000 Android apps collected from five Android markets demonstrate the effectiveness and scalability of our approach.

This is a repository copy of Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network.

This paper reports rational engineering of Trypanosoma rangeli sialidase to develop an effective enzyme for a potentially important type of reactivity: production of sialylated prebiotic glycans. The Trypanosoma cruzi trans-sialidase and the homologous T. rangeli sialidase has previously been used to investigate the structural requirements for trans-sialidase activity. We observed that the T. cruzi trans-sialidase has a seven-amino-acid motif (197–203) at the border of the substrate binding cleft. The motif differs substantially in chemical properties and substitution probability from the homologous sialidase, and we hypothesised that this motif is important for trans-sialidase activity. The 197–203 motif is strongly positively charged with a marked change in hydrogen bond donor capacity as compared to the sialidase. To investigate the role of this motif, we expressed and characterised a T. rangeli sialidase mutant, Tr13. Conditions for efficient trans-sialylation were determined, and Tr13's acceptor specificity demonstrated promiscuity with respect to the acceptor molecule enabling sialylation of glycans containing terminal galactose and glucose and even monomers of glucose and fucose. Sialic acid is important in association with human milk oligosaccharides, and Tr13 was shown to sialylate a number of established and potential prebiotics. Initial evaluation of prebiotic potential using pure cultures demonstrated, albeit not selectively, growth of Bifidobacteria. Since the 197–203 motif stands out in the native trans-sialidase, is markedly different from the wild-type sialidase compared to previous mutants, and is shown here to confer efficient and broad trans-sialidase activity, we suggest that this motif can serve as a framework for future optimization of trans-sialylation towards prebiotic production.

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. Fraud-Droid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼ 93%) and recall (∼ 92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.

The security of smart phones is increasingly important due to their rapid popularity. Mobile computing on smart phones introduces many new characteristics such as personalization, mobility, pay-for-service and limited resources. These features require additional privacy protection and resource usage constraints in addition to the security and privacy concerns on traditional computers. As one of the leading open source mobile platform, Android is also facing security challenges from the mobile environment. Although many security measures have been applied in Android, the existing security mechanism is coarse-grained and does not take into account the context information, which is of particular interest because of the mobility and personality of a smart phone device. To address these challenges, we propose a context-aware usage control model ConUCON, which leverages the context information to enhance data protection and resource usage control on a mobile platform. We also extend the existing security mechanism to implement a policy enforcement framework on the Android platform based on ConUCON. With ConUCON, users are able to employ fine-grained and flexible security mechanism to enhance privacy protection and resource usage control. The extended security framework on Android enables mobile applications to run with better user experiences. The implementation of ConUCON and its evaluation study demonstrate that it can be practically adapted for other types of mobile platform.