The growing number of Unmanned Aerial Vehicles or drones in low altitude airspace has opened multitude of frontiers in diverse applications such as smart city, disaster management, logistics, and surveillance operations. Nonetheless, the open and dynamic communication landscape leads the Internet of Drones (IoD) environment to many security threats including forgery, unauthorized access or physical drone hijacking attacks. One of the pressing challenges is to ensure perfect forward secrecy using symmetric crypto-primitives, since most of the conventional access control schemes rely on costly public key cryptosystems that might not be suitable for constrained environment. We can spot many lightweight key agreement mechanisms for IoD environment, however regrettably, security loopholes render those inappropriate for deployment. In this paper, we propose a lightweight access control mechanism for IoD environment leveraging Physically Unclonable Function (PUF) based on Barrel-Shifter (BS) architectures. The commutative properties of BS oriented PUF (BS-PUF) have been exploited to ensure perfect forward secrecy in PDCM-IoD. Moreover, it ensures privacy, revocation of rogue user’s identity, and resistance from known attacks including physical drone capture threats and forgery attacks. It significantly helps to reduce computational overheads in comparison with other IoD-based schemes. The security features are rigorously analyzed using RoR-based random oracle model. Overall, the PDCM-IoD supports 19.52% increased number of security features. The performance evaluation depicts that PDCM-IoD is highly suitable for IoD-based resource deficient ecosystem.

The rapid increase in electric vehicles (EVs) and the widespread deployment of charging stations have made secure authentication a critical requirement in Vehicle-to-Grid (V2G) environments. The growing interconnection among EVs, charging stations, and grid infrastructure introduces serious security and privacy challenges, including impersonation, replay, ephemeral secret leakage, and physical tampering attacks. Although several authentication protocols have been proposed for EV charging services, many existing schemes lack robust tamper-resistant and location-aware authentication capabilities and remain unsuitable for dynamic and resource-constrained V2G conditions. To address these limitations, this paper proposes a robust, tamper-resistant, and location-sensitive authentication protocol for securing EV charging services in V2G environments. The proposed protocol integrates configurable Arbiter Physical Unclonable Functions (A-PUFs) to provide device-level protection against physical tampering and unauthorized charging access. It also employs lightweight cryptographic primitives and techniques, including one-way hash functions, XOR operations, concatenation operations, and timestamp-based freshness verification, to support efficient mutual authentication and secure session key agreement. The security of the proposed protocol is evaluated through informal analysis and formal verification under the Random Oracle Model (ROM). Furthermore, comparative analysis demonstrates that the proposed protocol achieves a 17.16% reduction in communication cost and a 7.49% reduction in average computation cost compared with existing authentication protocols while maintaining strong security features. The results confirm that the proposed scheme enhances the security, efficiency, and practical deployability of EV charging authentication for next-generation V2G networks.

In AIoT-enabled secure and green supply chain systems, robust device authentication measures are crucial to maintaining the integrity of the ecosystem. One key challenge in this context is mitigating password guessing attacks—a scenario that can be modeled as a specialized sequence prediction task demanding high character-level accuracy and computational efficiency to safeguard devices and data. Although natural language processing (NLP) models, particularly GPT-based approaches, have excelled in sequence prediction tasks, they often lack the precision needed for password guessing due to their reliance on broad contextual dependencies. To address these limitations, we propose PassGAT, a novel framework that leverages Graph Attention Networks (GAT) to enhance password prediction performance. PassGAT transforms passwords into graph representations, where each character is treated as a node, enabling selective computation of attention coefficients between characters. This approach captures both local dependencies essential for character-level accuracy and global patterns that enhance the understanding of password structures. Experimental results demonstrate that PassGAT achieves an average improvement of 15.58% in accuracy over an existing GPT-based password guessing model while reducing computational overhead by 85.19%. By significantly enhancing authentication accuracy and efficiency, PassGAT provides a robust and sustainable solution to mitigate password-related security risks in AIoT-enabled supply chain systems.

Abstract In a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT‐enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well‐known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy‐preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self‐organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904‐906).

In the realm of miniature machine learning for Internet of Unmanned Aerial Vehicles (UAVs), the security concerns of machine learning models are obvious, especially when it comes to adversarial attacks. Models can become confused and their performance undermined by the introduction of meticulously crafted distortions. These attacks can even infiltrate a variety of models, bringing greater security risks. To understand how it works and mitigate its effects, our research focuses on scrutinizing the transferability of adversarial attacks in the expanding context of miniature machine learning for UAVs. In this paper, we introduce a formula help measure the transferability of adversarial attacks and explore ways to improve the transferability and effectiveness of adversarial attacks (e.g., a combination of attack techniques), and provide visulizations to vividly illustrate the repercussions of adversarial instances across a spectrum of attack intensities, helping facilitate more intuitive exploration and analysis of the results. For instance, our findings demonstrate that even subtle perturbations directed at specific attributes can lead to a significant decrease in model accuracy. We also evaluates the success rates of various attack algorithms and validates the proposed evaluation methodology for measuring transferability. And the outcomes unveiled in this study make noteworthy strides in fostering a profound comprehension of the transferability of adversarial attacks in the distinct realm of miniature machine learning for UAVs. Robust defense mechanisms, which ensure the impregnability of IoT-enabled UAV systems, can be cultivated by pinpointing the most efficacious attack strategies and evaluating their transferability.

The emerging Internet of Things (IoTs) and cloud technologies spark dramatic growth in efficiency and productivity for the conventional e-health sector. However, the extensive applications of the communication network also expose the sensitive medical data to the unprecedented cyber threats. To protect the data privacy in IoTs-based e-health cloud environments, we propose an adaptively secure data sharing scheme with traceability and equality test (T-ABEET). The T-ABEET not only allows flexible access control to the massive data but also provides the functionality of traitor tracing to identity the users who leak their decryption keys. Meanwhile, through carrying out the equality test, the target ciphertext can be retrieved efficiently without revealing anything about the plaintext. Particularly, distinct from previous traceable ABE works, the tracing cost in our T-ABEET scheme keeps constant even with the increasing number of users. Also, by introducing the multi-authority mechanism, our T-ABEET can avoid the inherent key escrow problem of ABE. Furthermore, our T-ABEET is demonstrated adaptively secure under subgroup decision assumption. Finally, performance comparison reveals that our T-ABEET has superior practicality, efficiency, and security in cloud-enabled e-health systems.

The rapid development of Artificial Intelligence (AI) has had a significant impact on various industries, including healthcare. The Internet of Medical Things (IoMT) has played a vital role in this evolution. However, while AI has contributed to many benefits in healthcare, concerns about data privacy and security persist. To address these concerns, we propose a framework that combines Federated Learning (FL) and Differential Privacy (DP) to enhance data protection within IoMT. By integrating FL’s decentralized approach with DP’s mechanism to prevent data reconstruction from model outputs, we can improve data confidentiality. This integrated approach is used to develop and analyze high-performing Convolutional Neural Networks (CNNs) for detecting Tuberculosis using chest X-ray datasets. The framework undergo thorough performance evaluation, utilizing various metrics to establish its superiority over baseline models. The results demonstrate the effectiveness of our framework as a robust solution for secure and private AI applications in healthcare.

Vehicle-to-grid (V2G) technology is used in the modern eco-friendly environment for demand response management. It helps in reducing the carbon footprints in the environment. However, security and privacy of the information exchange between different entities are significant concerns keeping in view of the information exchange via an open channel, i.e., Internet among different entities such as plug-in hybrid electric vehicles (PHEVs), charging stations (CSs), and controllers in V2G environment. With an exponential rise in Electric vehicles (EVs) usage across the globe, there is a requirement of developing a seamless charging infrastructure for charging and billing. Moreover, secure information flow needs to be maintained at different levels in such an environment. Hence, this paper proposes a blockchain-based demand response management for efficient energy trading between EVs and CSs. In this proposal, miner nodes and block verifiers are selected using their power consumption and processing power. These nodes are responsible for the authentication of various transactions in the proposal. We also proposed a game theory-based solution to support energy management and peak load control off-peak and peak conditions. The proposed scheme has been evaluated using various performance evaluation metrics where its performance is found superior in comparison to the existing solutions in the literature.

Abnormal or violent behavior by people with mental disorders is common. When individuals with mental disorders exhibit abnormal behavior in public places, they may cause physical and mental harm to others as well as to themselves. Thus, it is necessary to monitor their behavior using visual surveillance systems. However, it is challenging to automatically detect human abnormal behavior (especially for individuals with mental disorders) based on motion recognition technologies. To address these issues, in the current work, we propose an end-to-end abnormal behaviour detection framework from a new perspective in conjunction with the Graph Convolutional Network (GCN) and a 3D Convolutional Neural Network (3DCNN). Specifically, we first train a one-class classifier to extract features and estimate abnormality scores. To improve the performance of abnormal behavior detection, GCN is used to model the similarity between video clips for the correction of noisy labels. Then, based on this framework, GCN recognizes the normal behavior clips in the abnormal video and removes them, while the clips identified as abnormal behavior are retained. Finally, a 3D CNN is used to extract spatiotemporal features to classify different abnormal behaviors. In order to better detect the violent behavior of individuals with mental disorders, the paper focuses on the UCF-Crime dataset with various types of violent behaviors. By experimenting with this dataset, the classification accuracy reaches 37.9%, which is significantly better than that of the current state-of-the-art approaches.

The rising popularity of ICT and the Internet has enabled Unmanned Aerial Vehicle (UAV) to offer advantageous assistance to Vehicular Ad-hoc Network (VANET), realizing a relay node’s role among the disconnected segments in the road. In this scenario, the communication is done between Vehicles to UAVs (V2U), subsequently transforming into a UAV-assisted VANET. UAV-assisted VANET allows users to access real-time data, especially the monitoring data in smart cities using current mobile networks. Nevertheless, due to the open nature of communication infrastructure, the high mobility of vehicles along with the security and privacy constraints are the significant concerns of UAV-assisted VANET. In these scenarios, Deep Learning Algorithms (DLA) could play an effective role in the security, privacy, and routing issues of UAV-assisted VANET. Keeping this in mind, we have devised a DLA-based key-exchange protocol for UAV-assisted VANET. The proposed protocol extends the scalability and uses secure bitwise XOR operations, one-way hash functions, including user’s biometric verification when users and drones are mutually authenticated. The proposed protocol can resist many well-known security attacks and provides formal and informal security under the Random Oracle Model (ROM). The security comparison shows that the proposed protocol outperforms the security performance in terms of running time cost and communication cost and has effective security features compared to other related protocols.

Unmanned aerial vehicles (UAVs) with communication, computing, and storage capabilities have high mobility. Based on this advantage, it can push the service closer to the user. Our research group is concerned with implementing the Internet of Things (IoT) enabled massive crowd management platform that employs 5G to facilitate network connectivity among the UAV and sensory networks. In such a highly dynamic environment, IoT devices, users, and UAVs are the key factors to determine the caching strategies. Due to the limitations of drone batteries and changes in UAV cluster density, the environment is characterized as highly dynamic. However, the existing UAV caching strategy does not consider both the changes of the users and UAVs. Therefore, this paper proposes a three-layer UAV cache architecture in 5G network to achieve hierarchical adaptation to the dynamic changes of users and UAVs. Based on this architecture, we propose a dual dynamic adaptive caching(DDAC) algorithm. The DDAC algorithm is divided into two parts: user adaptation and UAV adaptation. For user adaptation, we designed a user-adaptive UAV trajectory model, which ensures the transmission efficiency of the UAV. For UAV adaptation, we designed and deployed a UAV-adaptive cache model based on a greedy algorithm in the cognitive center layer. The UAV can dynamically adjust the caching strategy according to the cluster density. Finally, the results of the experiment prove that our proposed UAV adaptive cache model has better performance in the cache hit ratio compared with the existing UAV cache model.

With the advantage of being close to the network, edge cloud-enabled computing mode brings flexibility to task scheduling. However, with the heterogeneity of computing resources between cloud and edge cloud, and the complexity of computing and communication processes between multi-edge cloud, challenges have been brought to the deployment and computing of tasks in cloud-edge collaborative environments. In order to solve this challenge, firstly a deep reinforcement learning controller based cloud-edge collaborative computing framework has been proposed. Then a system QoS model has been estab-lished considering both the user benefits and the service provider benefits. By using deep Q-network, a deep reinforcement learning based collaborative task placement algorithm has been proposed for dynamically optimizing the target system utility. Finally, the experimental results show that the proposed method has a good learning ability for the computing cost of cloud and edge cloud as well as the communication cost between multi-edge cloud. In addition, compared with Q-table learning, random computing and cloud computing, a 10% improvement of system utility has been achieved with the proposed method.

This article proposes a novel ultralarge-scale crowd monitoring system, namely, the ULCM system. The ULCM system enables advanced sensing and networking technologies aimed at collecting and processing multimodal, multiperspective, and real-time crowding data relevant to crowd management. This data will be further analyzed to provide a global realization of evolving events over a large geographical area as they occur in real time. The ULCM is the infrastructure component of an intelligent platform that is being developed by our research group to provide crowd intelligence to decision makers through an interactive digitized visual environment. In order to achieve a full comprehensive scene overview, the ULCM deployment utilizes a multiplicity of unmanned aerial vehicle (UAV) agents in different operational scenarios. The aerial deployment and control are realized by custom multiple UAV networks and airborne LiDAR sensors. The deployment and control on the ground sensory agents are based on multiple subnetworks, including closed-circuit television (CCTV) and infrared gas and ultrasonic sensors networks. Eventually, ULCM employs the software-defined network (SDN) and edge cloud technologies to optimize the networking and data analytics performance from the perspective of infrastructure.

With the rapid development of wireless communication and flight control technologies, the unmanned aerial vehicles (UAVs) have been widely used in multiple application scenarios. A typical scenario is massive crowd management of the multi-millions annual Hajj Pilgrimage to Mecca where UAVs are widely utilized to conduct crowd monitoring by carrying sensory devices. The safe flight of a UAV is crucial for ensuring the successful execution of missions. With the aim to overcome the disadvantage caused by the ground station intrusion detection, the combination of UAV and airborne LiDAR has been widely studied in the field of UAV obstacle recognition. This article studies the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm for UAV based on an airborne LiDAR (ALORID). First, the preprocessing of the data obtained by a LiDAR, i.e., the coordinate conversion of LiDAR data in combination with UAV motion parameters, is completed. Then, the LiDAR data graph at the current moment is generated by the image noisy point filtering algorithm. After that, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for image clustering of intrusions to obtain the LiDAR time-domain cumulative graph in a certain detection time. Finally, the motion recognition and location detection of each cluster are completed. The experiment results verify the effectiveness of the proposed algorithm in identifying the moving state of the intrusions.

In the recent years, mobile cellular networks are undergoing fundamental changes and many established concepts are being revisited. New emerging paradigms, such as Software-Defined Networking (SDN), Mobile Cloud Computing (MCC), Network Function Virtualization (NFV), Internet of Things (IoT), and Mobile Social Networking (MSN), bring challenges in the design of cellular networks architectures. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a scalable and efficient way. In this paper, first we discuss the limitations of the current LTE architecture. Second, driven by the new communication needs and by the advances in aforementioned areas, we propose a new architecture for next-generation cellular networks. Some of its characteristics include support for distributed content routing, Heterogeneous Networks (HetNets) and multiple Radio Access Technologies (RATs). Finally, we present simulation results which show that significant backhaul traffic savings can be achieved by implementing caching and routing functions at the network edge.

Line Speed Publish/Subscribe Inter-networking (LIPSIN) is one of the proposed forwarding mechanisms in Information Centric Networking (ICN). It is a stateless source-routing approach based on Bloom filters. However, it has been shown that LIPSIN is vulnerable to brute-force attacks which may lead to distributed denial-of-service (DDoS) attacks and unsolicited messages. In this work, we propose a new forwarding approach that maintains the advantages of Bloom filter based forwarding while allowing forwarding nodes to statelessly verify if packets have been previously authorized, thus preventing attacks on the forwarding mechanism. Analysis of the probability of attack, derived analytically, demonstrates that the technique is highly-resistant to brute-force attacks.