Performance optimization is a critical yet challenging aspect of software development, often requiring a deep understanding of system behavior and algorithmic tradeoffs, and careful code modifications. Although recent advances in AI coding agents have accelerated code generation and bug fixing, little is known about how these agents perform on real-world performance optimization tasks.

Third-party libraries like Log4j accelerate software application development but introduce substantial risk. Vulnerabilities in these libraries have led to Software Supply Chain (SSC) attacks that compromised resources within the host system. These attacks benefit from current application permissions approaches: third-party libraries are implicitly trusted in the application runtime. An application runtime designed with ZeroTrust Architecture (ZTA) principles - secure access to resources, continuous monitoring, and least-privilege enforcement - could mitigate SSC attacks, as it would give zero implicit trust to these libraries. However, no individual security defense incorporates these principles at a low runtime cost. This paper proposes Zero-Trust Dependencies to mitigate SSC vulnerabilities: we apply the NIST ZTA to software applications. First, we assess the expected effectiveness and configuration cost of Zero-Trust Dependencies using a study of third-party software libraries and their vulnerabilities. Then, we present a system design,$\text{ZTD}_{\text{Sys}}$, that enables the application of Zero-Trust Dependencies to software applications and a prototype,$\text{ZTD}_{\text{JAVA}}$, for Java applications. Finally, with evaluations on recreated vulnerabilities and realistic applications, we show that$\text{ZTD}_{\text{JAVA}}$can defend against prevalent vulnerability classes, introduces negligible cost, and is easy to configure and use.

This paper proposes a resource allocation method based on deep learning in power internet of things. The method uses density-based spatial clustering of noisy applications (DBSCAN) to analyze data, and then trains deep neural networks (DNN) based on the results of DBSCAN data. The results show that our method can significantly improve the user's quality of service (QoS).