CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps

Emerging Theranostic Nanomaterials in Diabetes and Its Complications

Diabetes mellitus (DM) refers to a group of metabolic disorders that are characterized by hyperglycemia. Oral subcutaneously administered antidiabetic drugs such as insulin, glipalamide, and metformin can temporarily balance blood sugar levels, however, long-term administration of these therapies is associated with undesirable side effects on the kidney and liver. In addition, due to overproduction of reactive oxygen species and hyperglycemia-induced macrovascular system damage, diabetics have an increased risk of complications. Fortunately, recent advances in nanomaterials have provided new opportunities for diabetes therapy and diagnosis. This review provides a panoramic overview of the current nanomaterials for the detection of diabetic biomarkers and diabetes treatment. Apart from diabetic sensing mechanisms and antidiabetic activities, the applications of these bioengineered nanoparticles for preventing several diabetic complications are elucidated. This review provides an overall perspective in this field, including current challenges and future trends, which may be helpful in informing the development of novel nanomaterials with new functions and properties for diabetes diagnosis and therapy.Peer reviewe

Preliminary expression profile of cytokines in brain tissue of BALB/c mice with Angiostrongylus cantonensis infection

BACKGROUND: Angiostrongylus cantonensis (A. cantonensis) infection can result in increased risk of eosinophilic meningitis. Accumulation of eosinophils and inflammation can result in the A. cantonensis infection playing an important role in brain tissue injury during this pathological process. However, underlying mechanisms regarding the transcriptomic responses during brain tissue injury caused by A. cantonensis infection are yet to be elucidated. This study is aimed at identifying some genomic and transcriptomic factors influencing the accumulation of eosinophils and inflammation in the mouse brain infected with A. cantonensis. METHODS: An infected mouse model was prepared based on our laboratory experimental process, and then the mouse brain RNA Libraries were constructed for deep Sequencing with Illumina Genome Analyzer. The raw data was processed with a bioinformatics’ pipeline including Refseq genes expression analysis using cufflinks, annotation and classification of RNAs, lncRNA prediction as well as analysis of co-expression network. The analysis of Refseq data provides the measure of the presence and prevalence of transcripts from known and previously unknown genes. RESULTS: This study showed that Cys-Cys (CC) type chemokines such as CCL2, CCL8, CCL1, CCL24, CCL11, CCL7, CCL12 and CCL5 were elevated significantly at the late phase of infection. The up-regulation of CCL2 indicated that the worm of A. cantonensis had migrated into the mouse brain at an early infection phase. CCL2 could be induced in the brain injury during migration and CCL2 might play a major role in the neuropathic pain caused by A. cantonensis infection. The up-regulated expression of IL-4, IL-5, IL-10, and IL-13 showed Th2 cell predominance in immunopathological reactions at late infection phase in response to infection by A. cantonensis. These different cytokines can modulate and inhibit each other and function as a network with the specific potential to drive brain eosinophilic inflammation. The increase of ATF-3 expression at 21 dpi suggested the injury of neuronal cells at late phase of infection. 1217 new potential lncRNA were candidates of interest for further research. CONCLUSIONS: These cytokine networks play an important role in the development of central nervous system inflammation caused by A. cantonensis infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-015-0939-6) contains supplementary material, which is available to authorized users

STGAT-MAD: Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGATMAD). The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks. Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales. Experiments on a new wind turbine dataset (built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times