Static program analysis for IoT risk mitigation in space-air-ground integrated networks

The space-air-ground integrated networks(SAGINs)are pivotal for modern communication and surveillance,with a growing number of connected devices.The proliferation of Io T devices within these networks introduces new risks due to potential erroneous synergistic interactions that could compromise system integrity and security.This paper addresses the challenges in coordination,synchronization,and security within SAGINs by introducing a novel static program analysis(SPA)technique using zero-knowledge(ZK)proofs.This approach ensures the detection of risky interactions without compromising sensitive source code,thus safeguarding intellectual property and privacy.The proposed method overcomes the incompatibility between SPA and ZK systems by developing an imperative programming language for SAGINs and a specialized abstract domain for interaction threats.The system translates network control algorithms into arithmetic circuits suitable for ZK analysis,maintaining high accuracy in detecting risks.Evaluations of real-world scenarios demonstrate the system's efficacy in identifying risky interactions with minimal computational overhead.This research presents the first ZK-based SPA scheme for SAGINs,enhancing security and confidentiality in network analysis while adhering to privacy regulations.

An Empirical Application of User-Guided Program Analysis

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive rate.The EUGENE tool can effectively lower the false positive rate.However,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent version.Additionally,people find it difficult to distinguish between true positives and false positives in the analytical output.In this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives.On the three projects ethereum,go-cloud,and kubernetes,we assessed our methodologies.According to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports.We paired our techniques with GoInsight~1 and discovered a vulnerability.We also offered a patch to the community.

An Action Analysis for Combining Partial Evaluation

This paper proposes an action analysis for implementing combining partial evaluation efficiently. By analyzing the results of binding time analysis, on erations, which should be used in the combining partial evaluation, are determined in advance, so that the computation in the combination of specialized programs is reduced effectively.