A primary challenge in network defense is to mine potential attack campaigns from massive, continuously arriving alerts and telemetry data in real time. This paper presents HoSig-Align I, an edge side unsupervised method designed for network streams to discover homologous blocks. Our approach innovatively fuses heterogeneous features like Internet Protocol (IP) and Payload into a unified representation while strictly excluding temporal information from it, only incorporating time via a dual time scale decay model during graph construction to capture temporal proximity. A density robust similarity is computed using an isolation style random partition forest, leading to a sparse k-Nearest Neighbors (k-NN) graph. The stream is then accurately segmented into internally cohesive and mutually isolated homologous blocks through spectral ordering and contrastive change point detection. Each block is encoded into a lightweight HoSig signature, forming the basis for cross organizational collaboration. Experiments on real network streams show that HoSig-Align I identifies coherent attack campaign blocks and improves separation and boundary clarity over baselines, while meeting low-latency and low-overhead requirements for edge processing.

Decentralized Autonomous Organizations (DAOs) signify a groundbreaking approach to Internet-based management, enabled by blockchain technology and cryptocurrencies, and are viewed as fundamental elements of the Web3 ecosystem. In this study, we delve into the concept of DAOs by thoroughly investigating their underlying structure, ideology, and operational principles. Furthermore, we present a novel DAO framework derived from a technical and organizational assessment and provide an overview of cutting-edge DAO tools currently available. This research enables the swift implementation of DAO creation or transformation customized to an organization’s specific stage. Additionally, we recognize current challenges and shortcomings in existing DAOs and propose areas for future exploration.

The rapid expansion of Internet of Medical Things (IoMT) networks has enabled continuous data collection from diverse medical sensors and devices, supporting real-time monitoring, diagnostics, and decision-making. However, the resource limitations of IoT nodes and the open nature of communication channels make healthcare data vulnerable to security and privacy breaches. To address these challenges, this paper presents a blockchain-assisted, privacy-preserving signature scheme leveraging Quantum Key Distribution $(\mathcal {QKD})$ to ensure secure and trustworthy data sharing in Healthcare Internet of Things (H-IoT) environments. The proposed scheme integrates a quantum-designated verifier signature mechanism with a private blockchain infrastructure, where peer nodes validate and store healthcare data securely. Formal (software-based) and informal security analyses demonstrate the scheme's resistance to forgery, replay, and quantum attacks. Simulation experiments conducted in Python show that the proposed protocol achieves strong cryptographic performance, with a computational cost of 42.1ms and a communication overhead of 834 bits. Additionally, a blockchain-based prototype implementation quantifies the time required to append various numbers of blocks and process multiple healthcare transactions, confirming the scalability and practicality of the proposed solution. The results affirm that the scheme offers a reliable, efficient, and quantum-resilient framework for securing sensitive medical data across distributed IoMT healthcare systems.

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.

Unmanned Aerial Vehicles (UAVs), play a significant role in the advancement of pervasive systems by providing efficient, scalable, and innovative solutions in various sectors, such as smart cities or location-based services. However, the current UAV delivery scenario presents various challenges for recipients, including lengthy identity verification processes, privacy concerns, and risks of fraud and theft. In response to these issues, this paper proposes an innovative system that leverages Blockchain technology and Adversarial Machine Learning (AML) to tackle these problems effectively. The proposed system streamlines the verification process, enhances privacy safeguards, and reduces fraud risks. The integration of AML is crucial as it enables users to have greater control over their personal data, boosting privacy and security. AML also plays a critical role in this system by creating test scenarios that reinforce the machine learning model against adversarial threats, ensuring its precision and dependability in the face of malicious manipulations. The paper also provides details on the practical implementation and evaluation of this system in real-life adversarial situations. The evaluation results demonstrate superior performance on selected metrics, highlighting the potential of this system as an effective solution for verifying recipients in UAV delivery.

FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system

Unmanned aerial vehicles (UAVs) have witnessed significant growth in various domains, such as agriculture, disaster management, and remote health management systems. However, the use of UAVs necessitates secure and efficient solutions that uphold privacy during task distribution. To address this challenge, this article introduces a novel architecture for privacy-aware task distribution in UAV communication systems. Our approach leverages the benefits of blockchain and smart token-based identification within the proposed architecture, ensuring decentralized, transparent, and tamper-proof operations. By adopting a crowdsourced task distribution model, our approach further optimizes task assignment among UAVs while prioritizing data privacy, user access control, and scalability. The architecture is designed to enhance fault tolerance, enabling seamless operation under dynamic and unpredictable conditions. We present a comprehensive implementation details of a proof-of-concept prototype of our proposed architecture, detailing its design and functionality. The experimental results demonstrate the feasibility, efficiency, and adaptability of our approach in diverse real-world scenarios, highlighting its potential for broader adoption across UAV applications.

In the rapidly advancing landscape of technology, the integration of Unmanned Aerial Vehicles (UAVs) with Artificial Intelligence (AI) algorithms holds immense potential, particularly in the realm of house inspections. The burgeoning demand for innovative solutions in the surveillance domain has propelled researchers to explore transformative approaches. However, despite recent strides, significant challenges persist in seamlessly incorporating drones with the Internet of Things (IoT) networks, particularly in addressing response time concerns associated with intricate tasks such as facial recognition and motion detection. This research paper seeks to bridge these existing gaps by proposing an avant-garde architectural framework that directly deploys Large Language Models (LLMs) onto drones. The pivotal motivation stems from the need to not only enhance the performance of AI-enabled drones but also to overcome the limitations tied to centralized AI. While specific quantitative metrics in comparison to existing state-of-the-art methods are not available, this innovative approach demonstrates the potential for significant improvements in efficiency for localized tasks. This highlights the qualitative advancements achieved through our research. By delving into the intricacies of surveillance applications, this research not only contributes to the optimization of house inspections but also charts a path toward the development of more responsive, secure, and efficient AI-enabled drones, thereby shaping the future landscape of UAV and AI integration in surveillance scenarios.

With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.

The traditional cloud computing paradigm faces challenges with the increasing number of Artificial Intelligence of Things (AIoT) devices generated at the network edge. Edge computing provides a novel approach to overcoming the limitations of cloud computing by delivering lower service latency and higher quality of service (QoS) to AIoT devices. However, edge computing encounters constraints due to scarce resources on edge servers and the long distance between AIoT devices and remote cloud servers. To ensure high QoS for AIoT devices, a balance is required between limited computing resources on the network edge and high latency caused by the geographic distance on the cloud side. In this paper, we propose an edge-cloud architecture that achieves optimal QoS for AIoT devices. We employ a federated learning-based architecture to train AIoT devices’ data locally, thereby ensuring privacy. We evaluate the effectiveness and efficiency of our proposed approach by comparing it with the centralized approach from the state-of-the-art using two widely used datasets. The experimental results demonstrate that our architecture achieves higher effectiveness and efficiency in improving AIoT devices’ QoS. Overall, our proposed edge-cloud architecture overcomes the limitations of traditional cloud computing, enhances user privacy, and delivers high QoS to AIoT devices.

The convergence of information technology (IT) and operation technology (OT) has made Industrial Control Systems (ICS) a popular target for cyberattacks in recent years. Unlike traditional networks, enhancing availability is the ICS network's top priority rather than confidentiality in the CIA scheme. We propose a bio-inspired adaptive defense framework based on dissimilar redundancy, diversity, and adaptive defense strategies to achieve this aim. The proposed mechanism mixed optimal network shuffling and cyber deception techniques to maximise the time attackers spend on the decoys. Besides, to provide an extra layer of protection for system availability, we introduce dual heterogeneous subnets in the proposed framework that could be regenerated once compromised. We evaluate the performance of the proposed defense framework in a typical industrial manufacturing network using an SDN-based platform and test the defense framework in various scenarios. Compared with previous research, the simulation shows a considerable improvement in defense performance in the adaptive defense mode.

Edge computing is becoming more and more popular in both academics and industries. With the booming of edge computing technology, the Unmanned Aerial Vehicle (UAV) based delivery system is expected to achieve higher efficiency and low latency. However, the UAV often collects user-specific data during the delivery process, the data-leakage or security/privacy breaching could occur during the data-sharing process between the edge nodes and UAV devices. Privacy-preserving issues are further refraining from the popularity of the UAV-based logistic systems. It is believed that the UAV tracking and identity verification system can provide imminent access-level security and privacy protection, which is urgently required to eliminate the practical concerns under the edge computing-based environment. To the best knowledge of authors, for the first time, this paper proposes a Self-Sovereign Identity (SSI) integrated framework with the latest Blockchain technology for UAV-based delivery system. It is expected to protect the edge computing-based UAV delivery system against security flaws and privacy concerns. In this work, the benefits of using SSI with Blockchain technology are analyzed, the efficiency of identifying and authenticating UAVs and their respective users is further experimented and discussed. The experimental results show that the integrated SSI framework with Blockchain can effectively improve the efficiency of the user identity management system as well as the identity verification process in the delivery process.

As the latest computing paradigm, edge computing has attracted increasing attention from both academia and industry in recent years. It significantly affects the design and development of many smart systems and challenges conventional security solutions. Therefore, a new security framework targeted for edge computing-based smart systems is urgently required. In this paper, we investigate various security issues in the edge computing-based Unmanned Aerial Vehicle (UAV) delivery system which is a typical system in smart logistics. Specifically, we focus on security issues related to abnormal intrusion detection, various security attacks, and authentication/access control. To address these security issues, we propose A2DSEC which is a novel security framework characterized by the capabilities such as detection, defense, and authentication. With A2DSEC, we can guarantee the security of user identity authentication, and provide effective early warnings so that corresponding security measures can be taken in a timely fashion. To verify the effectiveness of A2DSEC, we test and implement the core part of the framework on a real-world edge computing-based UAV delivery system. The experiment results show that the A2DSEC can effectively ensure the security of the UAV delivery system via the timely detection of a variety of attacks as well as unknown potential attacks.