ABSTRACT Multiaccess edge computing (MEC) reduces end‐to‐end (E2E) latency by offloading computation from user equipment (UE) to nearby edge hosts, but host‐selection policies in simulators such as Simu5G typically focus on service availability or CPU headroom. They do not explicitly account for dynamic UE–host latency and can therefore steer delay‐sensitive flows to topologically distant or temporarily congested hosts, inflating tail delay and limiting effective throughput. We extend Simu5G 's ETSI‐compliant MEC orchestrator with LatencyAwareSelectionBased , a lightweight host‐selection policy that jointly accounts for (i) a dynamic UE–host delay estimate derived from measurable run‐time delay statistics and smoothed via an exponential moving average and (ii) the host's normalized CPU load. These two signals are combined in a simple score, , controlled by a single trade‐off parameter . The policy is implemented as a plug‐in selection module activated through Simu5G 's native selectionPolicy hook, requiring only minimal changes to existing scenarios and preserving the standard orchestration pipeline. We evaluate the proposed policy in Simu5G 's MultiMecHost scenario under three operating points (Best, Moderate, and Worst) and an additional multitenant burst configuration with up to 20 UEs generating bursty arrivals. Using Simu5G scalar outputs, we take downlink MAC delay and per‐UE throughput as primary metrics and also examine CPU‐only, latency‐only, and mixed‐score variants for different values of . Across all configurations, the latency‐aware policy consistently maintains low tail latency (95th‐percentile downlink MAC delay below approximately ) while improving per‐UE throughput relative to Simu5G 's default CPU‐ and service‐based strategies and avoiding persistent host hot spots. The resulting design provides a simple, reproducible baseline for latency‐sensitive MEC orchestration and a practical integration point for future mobility‐aware and learning‐based host‐selection schemes.

ABSTRACT Cryptocurrencies represent a significantly utilized class of digital assets, encompassing a diverse array of tokens and coins available for trading purposes. In this study, the integration of machine learning algorithms with an arbitrage trading strategy across cryptocurrency exchanges is explored. The objective is to scrutinize prominent cryptocurrency pairs characterized by high volatility, vulnerability to speculation, regulatory gaps, liquidity constraints, and heavy‐tail distribution, with the intention of training the model to predict the potential for arbitrage. To differentiate from competitors who await rare arbitrage opportunities, a novel approach is introduced to enhance arbitrage profitability. The primary innovation of this study lies in demonstrating the capability to predict profitable arbitrage opportunities in discrete intervals in advance, by incorporating sophisticated confidence level metrics to initiate arbitrage trades only when the model's predictions demonstrate substantial certainty. The findings indicate that the profitability of the entire strategy exceeds 100% within a 1‐week timeframe.

ABSTRACTIn witness encryption (WE), an instance of an NP problem is allowed to be used to encrypt a message, and who holding a witness of the problem can efficiently decrypt the ciphertext. In this work, we put forth the concept of homomorphic witness encryption (HWE), where one can evaluate functions over ciphertexts of the same instance without decrypting them, that is, one can manipulate a set of ciphertexts with messages to obtain the evaluation of , for any function . We declare that such homomorphic witness encryption schemes can be generically constructed from indistinguishable obfuscation () for any classes of functions. Then we propose the instantiate of multiplicatively homomorphic witness encryption (MHWE) and linearly homomorphic witness encryption (LHWE) using an , homomorphic encryption for NP problems such as Subset‐Sum and a batch‐processed GS‐proof system, which enables us to evaluate multiplication operations and linear operations over ciphertext. Furthermore, we show the practicality of homomorphic witness encryption by proposing new protocols for applications of interest, such as homomorphic time‐lock encryption, multi‐party contract signing, and e‐voting.

ABSTRACTThe development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software‐defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk‐aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo‐honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti‐honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk‐aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively.

SummaryBlockchain technology has greatly promoted the development of the Internet of Medical Things (IoMT) from a centralized form to a distributed form. However, with many independent blockchain‐based IoMT (BIoMT) systems being established by different institutions, these heterogeneous blockchain systems have created a new “data islands” problem. This paper proposes a multi‐chain fusion (MCF) multi‐party governance model based on relay chain technology. The MCF model helps establish cross‐chain interaction and communication among the multiple heterogeneous blockchain systems in different medical institutions through the cross‐chain gateway. Meanwhile, by utilizing the inter‐blockchain transfer protocol (IBTP), the MCF model takes responsibility for legitimacy verification and reliable routing of cross‐chain transactions, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems. Then, an elliptic curve signature scheme is adopted to protect user privacy in cross‐chain transactions, which helps establish transaction verification for the MCF model in BIoMT systems. Finally, several experiments are carried out to prove the feasibility of the cross‐chain scheme and the security of the elliptic curve signature scheme, and the performance index of the cross‐chain system is evaluated. The performance results show that the MCF model can provide secure and efficient cross‐chain services for sharing medical data among heterogeneous BIoMT systems, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems.

Abstract To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross‐site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high‐quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two‐layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.

Abstract The conventional cloud‐centric Internet of Things (IoT) application fails to meet the latency requirement of time‐critical applications. The idea of edge and fog computing arrived to distribute workloads across the fog devices located in the local area. However, achieving seamless interoperability, platform independence, and automatic deployment of services becomes the major challenge over heterogeneous fog devices. This paper proposes an integrated and standards‐based fog computing federation framework, FogDEFT, that adapts OASIS–Topology and Orchestration Specification for Cloud Applications (TOSCA) for service deployment in fog. The framework standardizes the distributed application design with TOSCA Service Template to deploy Docker Containers in Swarm mode and manages interoperability over heterogeneous fog devices. The framework uses a lightweight TOSCA compliant orchestrator to dynamically deploy various fog applications (user‐developed services) on the fly.