Sixth Generation (6G)-enabled Internet of Vehicles (IoV) facilitates efficient data synchronization through ultra-fast bandwidth and high-density connectivity, enabling the emergence of Vehicle Twins (VTs). As highly accurate replicas of vehicles, VTs can support intelligent vehicular applications for occupants in 6G-enabled IoV. Thanks to the full coverage capability of 6G, resource-constrained vehicles can offload VTs to edge servers, such as roadside units, unmanned aerial vehicles, and satellites, utilizing their computing and storage resources for VT construction and updates. However, communication between vehicles and edge servers with limited coverage is prone to interruptions due to the dynamic mobility of vehicles. Consequently, VTs must be migrated among edge servers to maintain uninterrupted and high-quality services for users. In this paper, we introduce a VT migration framework in 6G-enabled IoV. Specifically, we first propose a Long Short-Term Memory (LSTM)-based Transformer model to accurately predict long-term workloads of edge servers for migration decision-making. Then, we propose a Dynamic Mask Multi-Agent Proximal Policy Optimization (DM-MAPPO) algorithm to identify optimal migration routes in the highly complex environment of 6G-enabled IoV. Finally, we develop a practical platform to validate the effectiveness of the proposed scheme using real datasets. Simulation results demonstrate that the proposed DM-MAPPO algorithm significantly reduces migration latency by$20.82\%$and packet loss by$75.07\%$compared with traditional deep reinforcement learning algorithms.

Software repositories such as PyPI and npm are vital for software development but expose users to serious security risks from malicious packages. The malicious packages often execute their payloads immediately upon installation, leading to rapid system compromise. Existing detection methods are heavily dependent on difficult-to-obtain explicit knowledge, rendering them susceptible to overlooking emergent malicious packages.In this paper, we present a lightweight and effective method, namely EMPHunter, to detect malicious packages without requiring any explicit prior knowledge. EMPHunter is founded upon two fundamental and insightful observations. First, malicious packages are considerably rarer than benign ones, and second, the functionality of installation scripts for malicious packages diverges significantly from those of benign packages, with the latter frequently forming clusters. Consequently, EMPHunter utilizes the clustering technique to group the unique installation scripts of new-uploaded packages and identifies outliers as candidate malicious packages. It then ranks the outliers according to their deviate degrees and the distance between each of them and known malicious instances, effectively highlighting potential malicious packages.With EMPHunter, we successfully identified 122 previously unknown malicious packages from a pool of 267,009 newly-uploaded PyPI and npm packages, achieving an mAP (Mean Average Precision) of 0.813 and an exceptional recall of 0.992 when auditing the top-10 rankings. All detected packages have been officially confirmed as genuine malicious package by PyPI and npm. We assert that EMPHunter offers a valuable and advantageous supplement to existing detection tools, augmenting the arsenal of software supply chain security analysis.

As the most popular Python software repository, PyPI has become an indispensable part of the Python ecosystem. Regrettably, the open nature of PyPI exposes end-users to substantial security risks stemming from malicious packages. Consequently, the timely and effective identification of malware within the vast number of newly-uploaded PyPI packages has emerged as a pressing concern. Existing detection methods are dependent on difficult-to-obtain explicit knowledge, such as taint sources, sinks, and malicious code patterns, rendering them susceptible to overlooking emergent malicious packages. In this paper, we present a lightweight and effective method, namely MPHunter, to detect malicious packages without requiring any explicit prior knowledge. MPHunter is founded upon two fundamental and insightful observations. First, malicious packages are considerably rarer than benign ones, and second, the functionality of installation scripts for malicious packages diverges significantly from those of benign packages, with the latter frequently forming clusters. Consequently, MPHunter utilizes clustering techniques to group the installation scripts of PyPI packages and identifies outliers. Subsequently, MPHunter ranks the outliers according to their outlierness and the distance between them and known malicious instances, thereby effectively highlighting potential evil packages. With MPHunter, we successfully identified 60 previously unknown malicious packages from a pool of 31,329 newly-uploaded packages over a two-month period. All of them have been confirmed by the PyPI official. Moreover, a manual analysis shows that MPHunter recognizes all potentially malicious installation scripts with a recall of 100% across all analyzed packages. We assert that MPHunter offers a valuable and advantageous supplement to existing detection techniques, augmenting the arsenal of software supply chain security analysis.