In recent years, the prevalence of medical insurance fraud has significantly impacted the national medical insurance fund. Furthermore, the task of medical insurance fraud detection faces considerable challenges due to the extreme imbalance of positive and negative samples. In practice, health insurance records contain longitudinal and cross-sectional multimodal data. Existing fraud detectors often disregard the multimodal information from heterogeneous neighbors (e.g., time, department, and treatment) and overlook the interaction details among multi-class data entities in claims behavior. To address these challenges, this study proposes the Heterogeneous graph-based Fraud Detection Network(HeFDN) for medical insurance fraud detection, which is based on heterogeneous graphs. The model represents the interaction behaviors of multi-class entities within medical insurance data through heterogeneous graphs. It also introduces a heterogeneous graph target node generator, termed HeGA, to address the issue of node imbalance classification in heterogeneous graphs. Leveraging the heterogeneous graph data structure, the model learns the topological structure and characteristics of actual minority class nodes by generating a set of virtual minority class target nodes. This approach aims to balance the data across different categories, thereby facilitating the distinction of minority class data in high-dimensional space. To validate the model’s effectiveness, this study conducted multiple experiments utilizing a public dataset and a dataset constructed from three real medical insurance sources from hospitals across various levels in Xianning City in China. Experimental results indicate that the proposed model enhances the AUC by an average of 18.2 % and the F1-Score by an average of 26.81 % compared to the most advanced algorithms reviewed.

Hyperledger Fabric, which satisfies decentralization and security, is one of the most popular blockchain distributed ledger technologies, but in high-throughput, high-concurrency distributed scenarios, concurrency conflicts can cause transactions to fail, resulting in significant network bandwidth and resource waste. In this paper, we deeply analyze the operation flow of Fabric’s underlying transactions, and propose a solution to concurrent transaction conflicts in Fabric based on distributed locks and message queues. Redis is used to implement the locking mechanism of concurrent keys, and the ChaincodeEvent event listener is used to detect the transactions and realize the unlocking operation; on this basis, the message asynchronous retry mechanism, i.e., use the message queue RabbitMQ to re-consume the conflicting transactions to solve the concurrent transaction conflicts encountered against Fabric at present. Experiments show that the proposed approach can significantly improve the concurrency capability of the system, including transaction success rate and effective transaction throughput, under different levels of transaction conflicts; it does not affect the performance of the system in the absence of transaction conflicts.

Android hybrid applications use the Web Kit engine on Android platform to render Web contents and process JavaScript codes, and enable JavaScript codes to access device sensitive resources and native APIs. Therefore, hybrid applications will bring different security issues compared to native applications. The existing privacy leak detection methods for Android native applications cannot be directly applied to hybrid applications. In addition to the Dalvik Virtual Machine (DVM), hybrid applications may also spread privacy in the WebView component, which increases the scope of privacy dissemination and increases the difficulty of taint tracking. We delve into the different characteristics of privacy spreading on the DVM, Webkit engine and JavaScript engine of hybrid ap-plications. We propose HTDroid, an efficient dynamic taint tracking system for hybrid applications. The core idea of HTDroid is to spread the taint tags in the DVM to the Web Kit engine, and add the function of tracking the spread of taint data to WebKit engine and JavaScript engine through source code instrumentation. Our experiments show that HTDroid can run on both Android emulators and devices and effectively detect privacy leaks in hybrid applications and can deal with the two ways of privacy leaks in hybrid applications while existing TaintDroid cannot. Compared to the original Android system, HTDroid incurs only 22% performance overhead on CaffenieMark benchmark and impose 4.3 % overhead on J avaScript V8 engine benchmark, which is acceptable for run-time taint tracking.

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.

Android usually employs the Secure Socket Layer (SSL) protocol to protect the user’s privacy in network transmission. However, developers may misuse SSL-related APIs, which would lead attackers to steal user’s privacy through man-in-the-middle attacks. Existing methods based on static decompiling technology to detect SSL security vulnerabilities of Android applications cannot cope with the increasingly common packed applications. Meanwhile, dynamic analysis approaches have the disadvantages of excessive resource consumption and time-consuming. In this paper, we propose a dynamic method to solve this issue based on our novel automatic traversal model. At first, we propose several new traversal strategies to optimize the widget tree according to the user interface (UI) types and the interface state similarity. Furthermore, we develop a more granular traversal model by refining the traversal level from the Activity component to the Widget and implement a heuristic depth-first traversal algorithm in combination with our customized traversal strategy. In addition, the man-in-the-middle agent plug-in is extended to implement real-time attack test and return the attack results. Based on the above ideas, we have implemented SSLDetecter, an efficient automated detection system of Android application SSL security vulnerability. We apply it on multiple devices in parallel to detect 2456 popular applications in several mainstream application markets and find that 424 applications are suffering from SSL security vulnerabilities. Compared with the existing system SMV-HUNTER, the time efficiency of our system increases by 38% and the average detection rate increases by 6.39 percentage points, with many types of SSL vulnerabilities detected.

Smartphone contains diverse sensitive private information, such as phone number, contact and credit. The Android platform employs permission mechanism to protect intensive information from illegal leakage. There are several tools to assist users to manage apps' permissions, such as App Ops, Privacy Guard, and XPrivacy. However, the permission mechanism of Android is complicated, and only a few of smartphone users are familiar with Android security mechanism. In addition, it is difficult for smartphone users to know whether the privacy information exposure by Android applications is legal or not. Reverse engineering and program data flow analysis are basic approaches to analysis applications. Although it needs analysts to manual analysis results, its efficiency is largely depends on analysts' skills and experience. To improve analysis efficiency, we take a case-based reasoning method to simulation the security expert analysis applications. We translate the result of Android static analysis tools into cases and build a case reasoning library. Experiments show that it can help analysts to aid analysis Android applications.