As blockchain technology grows more prevalent, its performance limitations have become a critical barrier to large-scale adoption. The rise of optimized heterogeneous blockchain systems has significantly increased the demand for fair performance testing frameworks. However, existing work, whether simulator-based or system-based, often relies on default settings, overlooking the impact of detailed configuration parameters, which can affect the fairness of performance evaluations. To fill this gap, we propose BlockAlign, a configuration alignment tool based on a rule tree, designed to enhance the fairness of performance testing across heterogeneous blockchain systems. Firstly, based on architectural analysis, we design a rule tree to filter, classify, and semantically align configurations. Secondly, we introduce a metric called fluctuation rate to measure performance differences before and after configuration alignment. Finally, we conduct experiments on Geth, Besu, and Conflux, demonstrating that BlockAlign significantly improves the fairness and credibility of performance comparisons.

Surface electromyography (sEMG) signal has been used in upper limb rehabilitation robots (ULRR). However, existing ULRR based on myoelectric controllers suffers from limited generalization ability in estimating three-dimensional (3-D) motion intention. This article proposes a muscle-synergy-inspired approach to enhance the generalization ability of the myoelectric controller of a cable-driven ULRR. Low-dimensional commands are extracted from sEMG signals based on an EMG-to-muscle activation model and non-negative matrix factorization. The extracted commands are used to estimate the 3-D human force. Two different trajectory tracking tasks are selected to test the generalization ability. The system is trained based on training sets where participants perform one task. Then the system is tested using testing sets where participants perform the other task. Finally, the system is verified on real-time robotic control experiment. Results show that the proposed controller achieves better force estimating accuracy, better trajectory tracking accuracy, and lower interaction force than the myoelectric controller without considering muscle synergies, which means the proposed controller yields better generalization performance.

The automatic detection of vulnerabilities in Web applications using taint analysis is a hot topic. However, existing taint analysis methods for sanitizers identification are too simple to find available taint transmission chains effectively. These methods generally use pre-constructed dictionaries or simple keywords to identify, which usually suffer from large false positives and false negatives. No doubt, it will have a greater impact on the final result of the taint analysis. To solve that, we summarise and classify the commonly used sanitizers in Web applications and propose an identification method based on semantic analysis. Our method can accurately and completely identify the sanitizers in the target Web applications through static analysis. Specifically, we analyse the natural semantics and program semantics of existing sanitizers, use semantic analysis to find more in Web applications. Besides, we implemented the method prototype in PHP and achieved a vulnerability detection tool called VulChecker. Then, we experimented with some popular open-source CMS frameworks. The results show that Vulchecker can accurately identify more sanitizers. In terms of vulnerability detection, VulChecker also has a lower false positive rate and a higher detection rate than existing methods. Finally, we used VulChecker to analyse the latest PHP applications. We identified several new suspicious taint data propagation chains. Before the paper was completed, we have identified four unreported vulnerabilities. In general, these results show that our approach is highly effective in improving vulnerability detection based on taint analysis.

Sandbox is an excellent tool for dynamic malware analysis. However, the sandbox detection techniques are increasingly adopted to develop malwares, which has been a significant threat to sandbox analysis. These malwares can detect the running environment and show different behaviors in corresponding environments. So far, there have been several studies about countermeasures, but most of them concentrate on Windows OS. Environmental features in Linux sandbox have not been summarized yet. Besides, existing popular sandboxes can hardly combat against sandbox detecting techniques. In this paper, we focus on Linux sandbox. We firstly propose Linux environmental features from six aspects and implement an effective tool to collect features from running environment to tell the discrepancy among physical machine, virtual machine and sandbox. More importantly, we present EnvFaker, an effective method to reinforce Linux sandbox against environmental-sensitive malware. This method uses tracer to track child process and injected process, filters to intercept sandbox detecting behaviors, and emulator to disguise wear-and-tear and network environment. The experimental results further demonstrate that our method is effective against detecting techniques for Linux sandbox.