As computer system software stacks become increasingly complex, the associated security risks also escalate. Trusted Execution Environments (TEEs) have emerged as a mainstream security solution to enhance system security. TEEs can be categorized into user-level TEEs, OS-level TEEs, and hybrid TEEs. However, these TEEs typically possess fixed security boundaries and isolation domains, limiting their adaptability to varying security requirements and dynamic scenarios. Moreover, the design of Trusted Computing Base (TCB) components in TEE frameworks often operates at the highest privilege levels of the architecture. This concentration of critical code at the highest privilege level increases the whole platform’s security risk due to the growing amount of code as more security functions are added. In this paper, we propose FlexClave, an extensible and secure TEE framework designed to address these issues. FlexClave leverages hardware primitives to create secure isolation boundaries tailored to different use cases. Additionally, our framework distributes TCB components across various privilege levels, reducing the concentration of security functions at the highest privilege levels and mitigating the risks associated with running extensive code in a single, highly privileged context. We implement two prototypes on ARMv9-A Fixed Virtual Platform and ARMv8 RK3399 SoC, each with two use cases (container and virtual machine), to evaluate the system’s security and performance.

Currently, Unmanned Aerial Vehicles (UAV) groups can quickly build a multi-hop transmission network, which have been widely utilized in emergency communication scenarios to perform search and rescue, environmental monitoring, personnel positioning, rapid networking, etc. In such emergency rescue situations, strict demands on real-time communication, security, and minimal resource consumption become paramount. Higher requirements for security, bandwidth, and real-time performance necessitate a secure and lightweight data transmission protocol. Additionally, due to the lack of personnel supervision in these scenarios, the probability of malicious nodes increases. Therefore, it is essential to quickly and proximally block malicious nodes’ data to prevent it from affecting subsequent network propagation, and to accurately identify the malicious nodes. To address these issues, in this paper, we propose a traceable, lightweight, and secure data transmission protocol for UAV multi-hop networks in emergency rescue scenarios. The proposed protocol can verify the integrity of data transmitted by a large number of nodes in real time, detect erroneous transmissions, and trace malicious users. Experimental results show that our protocol consistently outperforms the comparison schemes in terms of computational overhead. Moreover, in scenarios involving smaller groups (m=5) and fewer hops (n=4), it exhibits significantly lower communication bandwidth overhead than the reference methods. Security analysis using BAN logic and the formal verification tool Scyther indicates that the proposed scheme meets security requirements. Additionally, comparative analysis results demonstrate that the proposed scheme is highly effective and outperforms other related schemes under the unique constraints of emergency rescue scenarios, where rapid, secure decision-making and data transmission are critical.

With urban population density on the rise, emergency incidents are increasing in both frequency and complexity, placing growing pressure on existing rescue systems. Meanwhile, issues such as slow response times, inadequate coordination mechanisms, and inefficient information exchange further exacerbate the challenges faced by these systems. The integration of Vehicle-to-Everything (V2X) communication and 5G technology offers unprecedented capabilities, such as ultra-low latency and high data throughput, which are critical for real-time coordination and decision-making in emergency rescue scenarios. To establish secure and efficient vehicle communication in 5G and V2X-enabled emergency rescue scenarios, we propose an efficient vehicle secure scheduling and access authentication scheme based on certificateless cryptography and multireceiver signcryption. In this scheme, the command and control center can securely dispatch rescue fleets based on disaster conditions. By enabling mutual authentication and key agreement between rescue vehicles and roadside units, the scheme ensures the reliable and swift exchange of rescue information and instructions. In addition, to address unexpected situations such as traffic congestion, we design a route-switching mechanism. Furthermore, in order to mitigate potential malicious behavior, a vehicle legitimacy revocation mechanism is implemented to ensure the normal operation of the system. The security of the scheme is verified through formal analysis and informal analysis. Performance analysis demonstrates that the scheme offers significant advantages over existing ones in terms of signaling overhead, communication overhead, computational overhead, and energy efficiency.

Brain-inspired models have become a focus in artificial intelligence field. As a biologically plausible network, the recurrent neural network in reservoir computing framework has been proposed as a popular model of cortical computation because of its complicated dynamics and highly recurrent connections. To train this network, unlike adjusting only readout weights in liquid computing theory or changing only internal recurrent weights, inspired by global modulation of human emotions on cognition and motion control, we introduce a novel reward-modulated Hebbian learning rule to train the network by adjusting not only the internal recurrent weights but also the input connected weights and readout weights together, with solely delayed, phasic rewards. Experiment results show that the proposed method can train a recurrent neural network in near-chaotic regime to complete the motion control and working-memory tasks with higher accuracy and learning efficiency.

Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.