Last mile delivery by drones is a core component of innovative logistics systems, relying heavily on AI models for essential operations such as path planning and object recognition. However, models trained on region specific data often experience significant performance degradation when deployed in unfamiliar environments due to geographic domain shifts. This limitation impedes the rapid deployment of logistics networks and hinders model adaptation. Federated Learning (FL), as a distributed machine learning paradigm, enables multiple clients with diverse data to collaborate in training a global model. Nevertheless, within Mobile Edge Computing (MEC) environments, FL faces critical challenges, including data bias, high drone mobility, and intermittent communication windows between drones and edge servers. This paper proposes FedMO, a mobility aware FL framework for drone based last mile delivery with edge cloud collaboration. FedMO introduces a novel algorithmic insight by treating the drone’s flight path as a unified proxy for both communication reliability and data distribution heterogeneity. The framework implements a synergistic three stage selection policy that jointly optimizes connectivity success, data value, and resource efficiency. This effectively transforms mobility from a disruption risk into a diversity enhancing asset. Experimental results using real world drone video datasets demonstrate that FedMO improves convergence speed by approximately 15% compared to baseline methods with only 30%-40% client selection. With equivalent client participation, FedMO achieves a 25% improvement in convergence speed over the FedAvg algorithm.

The rapid expansion of the Internet of Things (IoT) marketplace requires intelligent and adaptable infrastructures capable of efficient resource allocation and management among diverse entities, such as electric vehicle charging stations and energy providers. As the IoT marketplace increasingly intersects with critical electricity and transportation infrastructures, the need for coordinated, intelligent decision-making becomes more urgent. Despite its promising potential, the IoT marketplace currently faces critical challenges, including inefficient resource allocation, system congestion, and unpredictable patterns of energy demand, all of which hinder its seamless operation. In response, this paper proposes an innovative AI-driven framework for the IoT marketplaces that integrates context-aware techniques with Q-learning to transform resource allocation and matchmaking processes. Using EV charging as the primary case study, we implement context-aware similarity matching to accurately pair EVs with optimal charging stations, while Q-learning algorithms dynamically enhance matchmaking decisions. Our experimental results clearly demonstrate that this integrated approach effectively reduces charging delays, optimizes energy allocation, and substantially improves overall system efficiency. This research marks a significant advancement toward an intelligent and agile IoT marketplace infrastructure, which addresses critical challenges and supports the sustainable evolution of future electricity and transportation infrastructures.

The automotive industry increasingly relies on Over-the-Air (OTA) updates to deliver essential security patches to vehicles. However, this dependence may introduce significant cy-bersecurity vulnerabilities, particularly concerning the integrity and privacy of updates. This paper presents an integrated framework that combines threat modeling and formal verification by employing an identical system model across all stages. The process begins with applying the ThreatGet tool to identify potential threats in the OTA update process, which directly guide the formulation of formal security requirements expressed as Computation Tree Logic (CTL) properties. The same high-level model encompassing a Cloud Server (CS), Telematics Control Unit (TCU), Central Gateway Unit (CGU), Advanced Driver Assistance System (ADAS) module, and an attacker module is consistently used for both threat modeling and encoding in the NuSMV model checker. This unified approach ensures that identified threats translate seamlessly into verifiable properties. Experimental threat analysis and verification results demonstrate the effectiveness of our integrated approach in uncovering OTA update vulnerabilities properties.

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.

Industrial Control Systems (ICS) are increasingly targeted by distributed denial-of-service (DDoS) attacks, posing significant risks to system availability and reliability. This research proposes a novel defensive framework for ICS networks based on Software-Defined Networking (SDN). The main objectives are to enhance resilience against DDoS attacks and maintain critical system functions. Our framework combines automated traffic control (ATC) to filter and bypass malicious traffic dynamically, and Moving Target Defense (MTD) techniques such as proactive IP shuffling and network redundancy to protect critical nodes. Experimental results show that the proposed approach effectively reduces CPU load, improves round-trip time (RTT), and lowers packet drop rate (PDR) during DDoS scenarios. These findings demonstrate that integrating SDN-based ATC and MTD strategies can significantly strengthen ICS security and ensure system availability, providing a robust solution for critical infrastructure protection.

Ransomware has emerged as a significant security threat in cyberspace, inflicting severe economic losses and privacy breaches on individual users and organizations. Ransomware typically encrypts critical user files and demands a ransom for decryption. Traditional signature-based defense methods effectively identify known ransomware but perform poorly when confronting unknown zero-day attacks. Addressing this challenge, a ransomware detection framework called ‘RansoGuard’ is proposed. This framework aims to achieve timely identification and defense against ransomware by capturing and analyzing the sensitive Application Programming Interface (API) call behavior exhibited before the encryption attack is launched. A real-world ransomware sample dataset was constructed. The dynamic behavioral data during the pre-attack stage was analyzed, and natural language processing techniques were used to represent and extract key features from API call sequences. A Recurrent Neural Network (RNN) classifier was trained on these features to distinguish ransomware from benign software. Experimental results demonstrate that the RansoGuard framework exhibits outstanding early ransomware detection performance across different datasets, achieving a recall of 96.18% and an accuracy of 94.26%. Furthermore, it exhibits robustness in effectively countering zero-day attacks.

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.

Biometric technology has driven the rise of Biometrics as a Service (BaaS) due to its unique security and convenience. However, in the traditional cloud-based BaaS systems, raw biometric data leakage and biometric efficiency issues are still concerns, which may reduce user trust and engagement in biometric services. In this paper, we propose an innovative BaaS system named Bio-CEC in a Cloud-Edge Cooperative environment. Bio-CEC features a novel biometric template transformation scheme, rooted in multivariate polynomial transformation and random virtual feature replacement. This innovative scheme enables the revocation and regeneration of biometric templates, enhancing security in case of system breaches. For the biometric template protect scheme, a template matching algorithm using filtering operations is proposed, aiming to facilitate secure and accurate authentication within the transformation domain. Our comprehensive experiments and in-depth safety analysis verify the superiority of Bio-CEC. The results clearly demonstrate that Bio-CEC outperforms traditional cloud-based biometric system and provides a safer and more efficient solution for practical biometric system applications.

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.

In recent years, ransomware attacks have exploded globally, and it has become one of the most significant cyber threats to digital infrastructure. Such attacks have been targeting ranging from individuals to critical infrastructure or large organizations such as large commercial companies, energy facilities, medical centers and government departments. Ransomware attackers use sophisticated encryption techniques to hijack victims’ files in exchange for a large ransom to release encrypted data. Sophisticated encryption techniques make it almost impossible for victims to recover data without the secret key in the event of such an attack. To protect systems from ransomware threats, malicious activities had better be detected earlier, preferably before they engage in the harmful behavior. Numerous studies have focused on ransomware threats and attempted to provide detection and prevention solutions for ransomware attacks, but none of the surveys explored the early detection of ransomware and highlighted challenges and issues with existing solutions. This survey fills this gap and provides a state-of-the-art overview of research on the ransomware early detections. Moreover, we investigate the latest ransomware surveys and give an overview of the categories of ransomware from different perspectives, the evolution and attack process of ransomware, and provide datasets used for ransomware detection. Finally, the possible future research directions are discussed.

In recent years, Industrial Control Systems (ICSs) have faced increasing vulnerability to cyber attacks due to their integration with the Internet. Despite efforts to enhance cybersecurity, reconnaissance attacks remain a significant threat, prompting the need for innovative defensive strategies. This paper introduces a novel approach to strengthen the defensive capabilities of ICS networks against reconnaissance attacks using machine learning-driven cyber deception techniques. Leveraging Conditional Generative Adversarial Networks (CGANs), the proposed framework dynamically generates defensive network topologies to network shuffling and implement deception strategies, prioritizing system availability. Extensive simulations demonstrate the superior efficacy of the proposed framework in enhancing cybersecurity while minimizing computational overhead. By effectively mitigating reconnaissance attacks, this solution reinforces the resilience of ICS networks, safeguarding critical industrial infrastructure from evolving cyber threats. These findings underscore the significance of adopting machine learning-based cyber deception as a pragmatic security measure for protecting ICS networks in real-world industrial contexts.

Ransomware attacks, which blackmail victims into paying a ransom by locking their devices or encrypting their files, have become one of the major threats to network security. Conventional anti-ransomware tools often fail to detect zero-day ransomware attacks due to the inability to obtain zero-day ransomware signatures in advance to train detection models. In addition, zero-day ransomware attacks often use sophisticated encryption techniques to launch attacks on new vulnerabilities, and these encryption attacks cause irreversible damage to victims' digital files even if they choose to pay a ransom. It is therefore urgent and important to identify unknown ransomware attacks as early as possible, i.e. before the stage of encryption. To this end, this paper proposes Zero-Ran Sniff (ZRS), an early zero-day ransomware detection method based on zero-shot learning, which can detect zero-day ransomware attacks in the early stage. ZRS leverages the portable executable header (PE header) feature from executable files to identify ransomware. It comprises two stages: an auto-encoding network-based core attribute learning (AE-CAL) stage and a self-attentive mechanism-based convolutional neural network inference Stage (SA-CNN-IS). During the AE-CAL stage, the core features of known and unknown classes of ransomware are extracted using self-encoding networks, and the SA-CNN-IS phase identifies ransomware. To the best of our knowledge, we are the first to explore the use of zero-shot learning for zero-day ransomware early detection. Experimental results demonstrate that the proposed ZRS outperforms traditional machine learning methods. Compared to previous zero-day detection work, ZRS achieves a recall of 98.47% and an accuracy of 96.31%

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.

National digital identity systems (NDIDs) are increasingly important for users’ authentication and secure access to e-government services. However, there is insufficient research on human-centric cybersecurity (HCCS) that impacts the use of NDIDs. Drawing on the theory of planned behavior and technical formal informal model, this paper proposes and validates a research model that depicts how HCCS affect the use of NDIDs. Data were collected from 203 Australian residents and analyzed using structural equation modeling and multiple linear regression analysis. The findings revealed that security, privacy, perceived risk, usability, flexibility, and cultural and social interference significantly impact the use of NDIDs. Considering HCCS in NDIDs usage, especially in risk-conscious cultures, is crucial. Low cybersecurity awareness and trust impede NDIDs adoption, emphasizing the need for cybersecurity education and awareness. The insights benefit policymakers, governments, and cybersecurity practitioners, providing a valuable understanding of human-centric cybersecurity influence on the use of NDIDs.

Zero trust cybersecurity is beginning to replace traditional perimeter-based security strategies and is being adopted by organizations across a wide range of industries. However, the implementation of zero trust is a complex undertaking, different from traditional perimeter-based security, and requires a fresh approach in terms of its management. As such, a clear set of critical success factors (CSFs) will help organizations to better plan, assess, and manage their zero trust cybersecurity. In response, we investigated the CSFs for implementing zero trust cybersecurity by conducting a three-round Delphi study to obtain the consensus from a panel of 12 cybersecurity experts. We built a multi-dimensional CSFs framework that comprises eight dimensions, namely identity, endpoint, application and workload, data, network, infrastructure, visibility and analytics, and automation and orchestration. Based on the CSFs, we developed a maturity assessment framework enabling organizations to evaluate their zero trust maturity. This paper contributes to a theoretical understanding of how to deploy zero trust from multiple dimensions and offers a viable guidance framework for organizations from a practical perspective. This paper is useful for organizational stakeholders who are in the process of planning, reviewing, or implementing zero trust cybersecurity.

This paper establishes the current state of human-centric cybersecurity factors that influence users’ adoption of national digital identity systems (NDIDs). NDIDs are national-level security systems that provide digital identity management services for secure authentication and access to online government services. Advances in NDIDs have raised concerns about human-centric cybersecurity factors. These concerns motivated researchers to explore the human aspects of cybersecurity. This paper critically synthesizes the literature on human-centric cybersecurity factors to enrich our knowledge of why users adopt or reject NDIDs. This paper identifies a combination of trust, privacy, perceived risk, usability, flexibility, cultural and social interference, and security factors that influence the adoption of NDIDs. This study builds a multi-level conceptual framework to contextualize human-centric cybersecurity factors influencing NDIDs adoption. This paper contributes to current literature and recommends that future research should consider non-technical aspects of cybersecurity that affect NDIDs adoption.

Due to millions of heterogeneous physical nodes, multiple-vendor and multi-tenant domains, and technologies etc., 5G has greatly expanded the threat landscape. Particularly from the high rate of traffic and ultra-low latency requirement of applications in 5G networks, the detection of the network traffic anomalies in real-time is critical. The conventional security approaches lack compatibility with modern network designs and are not much effective in 5G settings. We propose a two-stage network traffic anomaly detection system compatible with ETSI-NFV standard 5G architecture. Our architecture consists of two modules, i.e., (a) Dimensionality Reduction to compress the sample size at the edge of 5G networks and (b) Deep Neural Network classifier (DNN) that detects traffic anomalies. We have conducted our experiments using OMNET++ and ETSI-NFV (OSM MANO) 5G orchestration real platform deployed on AWS cloud systems. We have used the UNSW-NB15 data set and have shown that at dimensionality reduction factor of 81% the detection accuracy obtained is 98%. The proposal is compared with other recent approaches to show the overall merit of the architecture.

The integration of Internet of Things (IoT) with 5G simply creates additional threat landscape and any network infrastructure is more vulnerable. Severe attacks on networks potentially damage organization reputation, customers or tenants lose confidence, and impacts operational and maintenance cost. Intrusion detection systems (IDSs) are an effective approach to mitigate threats. We present a novel IDS mechanism in which the unique Radio Frequency (RF) features of IoT devices are used to create a learning model which is later used to identify the illegitimate devices in the network. Leveraging the Deep Autoencoder (DAE), the existing steady-state feature extraction is generalized. The performance evaluation is conducted using a real data set from different aspects including the mobility of the nodes. The proposed IDS is broken down into pluggable virtual network function (VNF) components and its evaluation is presented for its integration into the 5G network slicing ecosystem from the perspective of the European Telecommunications Standards Institute (ETSI) standards. A Proof of Concept (PoC) is presented using ETSI Open Source NFV Management and Orchestration (OSM-MANO) test bed, deployed on AWS cloud systems, to show how the proposed approach would fit in with a real-life MANO.

The selfish On-Board-Unit (OBU) attacks Vehicular Ad-Hoc Network (VANET) by various attacks for profit. However, many existing methods are based on the principle of direct reciprocity for communication, and when an attack occurs, it is easy to crash in the case of large-scale networks. In order to reduce the number of attackers in the vehicle ad-hoc network and restrain the attack motivation of the OBUs, we propose an indirect reciprocal incentive mechanism based on reputation to encourage the OBUs in the VANET to help each other. Since most OBUs are in great need of network services, including potential attackers, when the loss of network services is far greater than the illegal benefits of their attacks, selfish and rational OBU will give up attacks and take desirable behavior. In addition, to prevent some attacks from tampering with information, we also apply blockchain technology to record the behavior of OBU. The indirect reciprocity process of each OBU in VANET can be regarded as a Markov Decision Process (MDP). In order to restrain the attack motivation of selfish nodes and communicate normally without knowing the attack model, an algorithm based on Deep Reinforcement Learning (DRL) is proposed to suppress attack motivation, so as to activate OBU learning in dynamic environment and make wise decisions. Finally, through a large number of simulation experiments, the performance of our proposed algorithm is obviously better than that of the baseline strategy, and is verified by the simulation results.

User perceptions of national digital identity systems (NDIDs) significantly impact their use and acceptance. Previous study on the use of NDIDs has provided limited frameworks for future research, with a strong emphasis on government services as well as how the system may be improved. This study evaluates how human-centric cybersecurity factors influence the use of NDIDs and acceptance among users. For instance, MyHealth record, which is used in Australia to record medical services provided to users, was overwhelmingly rejected by users due to concerns about digital identification information being used without authorisation and other privacy concerns. We hypothesise that human-centric cybersecurity factors influence the use of NDID and acceptance among users. The study also has a practical implication since it provides a framework to determine human-centric cybersecurity factors that influence adoption and improve NDIDs usage.

This Special Collection aims at seeking original articles with novel perspectives and solutions to address the cryptographic security and information hiding technology for Cloud or Fog-based IoT system. We expect this Special Collection can provide scientists, researchers, and industrial practitioners with a chance to publish original manuscripts that demonstrate and explore current advances in all aspects of security, privacy, trust and covert communication issue for Cloud or Fog computing/architecture IoT system.

The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today’s digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.

Summary The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.

Blockchain has transitioned beyond the hype to reality, as evidenced by the amount of research it has attracted and by its commercial applications. One popular application of blockchain is in cybersecurity, which is the focus of this paper. Specifically, we performed a systematic literature review of blockchain use cases for cybersecurity, while focusing on articles published over the past decade. Based on our analysis of 111 articles, we developed a classification framework using the thematic analysis approach. This classification framework is designed to offer readers a comprehensive perspective of the potential of blockchain to enhance cybersecurity in different contexts. The findings have implications for research and practice.

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.

AI (Artificial Intelligence) technology has been widely used in smart systems which usually require computing services with high availability and fast response. However, the rapid growth of data and service requests generated by end devices brings critical challenges to the centralised cloud computing paradigm in terms of network bandwidth, reliability and response time. In addition, the problem of data privacy is arising due to a large amount of data being transferred to the cloud server. Recently, edge computing is becoming a popular platform for smart systems as its provisions computing services close to the end devices, and Federated Learning (FL) is emerging as a promising solution for AI applications to address the data privacy issue. Inspired by their success, in this paper, we propose an edge based FL framework named Fed-UAV to solve the person reidentification problem in the UAV delivery service which is a typical AI application in smart logistics. This framework enables the UAV to efficiently locate the target receivers, and effectively reduce the data transmission between the UAV and the cloud server to improve the response time and protect the data privacy. Comprehensive experiments are conducted on three real-world datasets, and the experimental result successfully demonstrates that Fed-UAV can achieve both high accuracy and efficiency in person re-identification while protecting data privacy.

The real-world data sets often leveraged by Federated Learning (FL) applications are mostly non-independent and non-identically distributed (non-IID). This usually results from the diverse nature of the participating clients and their individual data-gathering contexts. An effective FL algorithm must incorporate the capability to produce a joint model that generalizes and captures these diverse patterns. In this work, we show how using some wild external data samples as placeholders for missing classes on client devices can alleviate the learning difficulty often posed by inbalance data distributions. Our exploration showed that this strategy enhances learning and can significantly boost test accuracy, particularly in extreme label skew scenarios. We recorded over 25% reduction in test error rate for the pathological non-IID partitions of the CIFAR10 data set. Our results are similar to those obtainable through bound-expanding strategies such as direct data sharing among clients. But unlike these techniques, our approach rules out the risk of exposing client's private data.

One of the challenges to the adoption of the decentralised approach to digital ID is a lack of consensus and standardisation of how different stakeholders within the ecosystem can inter-operate. As a means to address this issue, we examine the use of standard application programming interfaces (API) to integrate decentralised digital identification systems to preexisting ones. We first examine the current literature and solutions to (a) assess the attributes necessary to compare and contrast APIs, and (b) create a list of API providers within the decentralised digital ID marketplace, (c) compare the API providers against the attributes established. Based on an API Usability and Adoption framework as our lens, we assessed 19 service providers of APIs against their use cases. We identified that whilst the APIs are maturing, the APIs remain inconsistent and poorly adopted. A clear standard API could assist in better adoption. The guidance provided can inform organisations implementing digital identity and VCs along their adoption journey

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.

In recent years, with the increase of industrial volume, problems such as difficult management and low efficiency have become obstacles to the development of industry. Many studies have shown that industry can complete the automation of industry by combining with Internet of Things, that is, Industrial Internet of Things (IIoT), which will contribute to the secondary development of industry. However, in IIoT, there are also malicious attacks against industrial devices or sensors, such as DDoS, Sybil, etc., which will cause great security risks to the system. As a popular security trust framework, blockchain technology will face the dilemma of low throughput and limited energy if it is directly applied in the IoT devices. Based on this, in view of the existing IIoT security problems and the difficulties of traditional blockchain based on chain application, this paper proposes a blockchain security solution based on Directed Acyclic Graph(DAG) structure for IIoT, and combines differential privacy technology to further ensure the privacy and integrity of data. In order to ensure the stability and anti-interference ability of the network, this paper also proposes a load balancing algorithm, which effectively balances the relationship between node power consumption and network lifetime. Finally, the simulation results show that the proposed scheme is superior to the chain-blockchain scheme in network performance and power consumption. The average network delay is reduced by 24%, and the average network lifetime is increased by 48%.

With the rapid development of Internet and mobile communication technologies, the social media has been spreading into all aspects of people's life, work and study. People can not only communicate with each other and share news through social media platforms, but also check news and information and learn about popular topics. However, due to the social platform's characteristics, such as rapid information dissemination and interaction mechanism among users, it has also become the main attacking surface for adversaries. Spammers mainly use social media platforms to spread phishing, fraud, publish pornography, malicious content and links to make profits, which seriously disrupts the normal operation of social media platforms and causes adverse effects on society. In this paper, we combine the semi-supervised model with maximum contrastive pessimistic likelihood (MCPL) estimation and the ensemble learning CatBoost algorithm, proposing a semi-supervised ensemble learning classification algorithm model (SSML-CatBoost) for spammer detection in social media, and comparative experimental results show that our model outperforms other models for different amounts of labeled data and different numbers of iterations.

Abstract Nowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV‐based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high‐speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC‐based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC‐based UAV delivery system. Specifically, we propose an energy‐aware decision‐making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real‐time system status and QoS constraints, and through a dynamic two‐tier decision‐making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real‐world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.

Advanced persistent threat (APT) for cyber espionage poses a great threat to modern organizations. In order to mitigate the impact of APT on an organization, all the compromised systems in the organization must be quarantined and recovered in a timely and effective way. This article focuses on the problem of customizing a dynamic quarantine and recovery (QAR) scheme for an organization so that the APT impact is minimized. Based on a novel node-level epidemic model characterizing the effect of the QAR scheme on the expected state of the underlying network, we estimate the expected impact of APT under a QAR scheme. On this basis, we model the original problem as an optimal control problem. By use of optimal control theory, we derive the optimality system for the optimal control problem and thereby introduce the concept of normal potential optimal (NPO) control. Next, through comparative experiments, we find that the NPO control outperforms a set of heuristic controls. Hence, the QAR scheme associated with the NPO control is satisfactory in terms of the effectiveness of defending against APT. Finally, we examine the effect of some factors on the expected APT impact under the NPO control. This article would be helpful to the defense against APT for cyber espionage.

Most applications of Internet of Vehicles (IoVs) rely on collaboration between nodes. Therefore, false information flow in-between these nodes poses the challenging trust issue in rapidly moving IoV nodes. To resolve this issue, a number of mechanisms have been proposed in the literature for the detection of false information and establishment of trust in IoVs, most of which employ reputation scores as one of the important factors. However, it is critical to have a robust and consistent scheme that is suitable to aggregate a reputation score for each node based on the accuracy of the shared information. Such a mechanism has therefore been proposed in this paper. The proposed system utilises the results of any false message detection method to generate and share feedback in the network, this feedback is then collected and filtered to remove potentially malicious feedback in order to produce a dynamic reputation score for each node. The reputation system has been experimentally validated and proved to have high accuracy in the detection of malicious nodes sending false information and is robust or negligibly affected in the presence of spurious feedback.

In this note, the further improvements to prior work in the area of geographical avoidance within the Tor network have been conducted, that aims to improve the security and performance of such systems. First, we propose a new approach to a prior method where the round-trip time from client to entry is directly measured rather than estimated. Secondly, we introduce a new system where we are able to extrapolate data collected from a comparatively small number of Tor circuits and apply it to other unmeasured connections. A dynamic threshold has been identified to partially compensate for inaccuracy and shifts up or down depending on the length of the circuit. The experimental results quantitively validate the effectiveness and efficiency of the new proposed system. The testbed produced in this work may prove useful to future research in the areas of traffic analysis, the Tor network, and geographical avoidance on the Internet.

Summary This research presents a novel framework comprising the IPS gateway, analysis system, and honeypot for identifying and detecting ransomware based on the client honeypot concept, and active interception of downloads using Suricata inline intruder prevention system. Unlike previous frameworks that report on the accuracy rate of detecting ransomware, the proposed framework features a multiple voting platform for the validation of confidence levels in the accuracy detection rates. The proposed framework achieves high accuracy levels than other machine learning models for the detection of ransomware.

Summary Malware refers to any software, programs, or files that are intentionally utilised to compromise the system and cause unexpected losses to end‐users such as economical losses or privacy breaches. The rapid growth of malware makes it impossible to keep up with its progress merely via human interventions or manual analysis. One of the challenges for the human‐oriented approaches is they will cause backlog and inability to keep up with the development traces of the malware. Hence, an efficient method is needed urgently to analyse effectively and identify accurately the malware in their domain. Malware clustering has been extensively studied in the machine learning area with regards to distance functions, grouping algorithm and cluster validation. A large number of research studies have been done via behavioral analysis for clustering to achieve high performance of malware detections. However, there is a trade‐off for better detection performance between behaviorial approaches and high computational forces. Up to date, little work focuses on the deep learning representations for malware clustering. Therefore, in this paper, we propose an enhanced deep embedded clustering method to facilitate an effective and efficient malware clustering process. The new method takes advantage of linear dimensionality reduction and a customised deep neural network to learn malware representations in an orthogonal space and performs cluster assignments. Our experimental results demonstrate that the proposed clustering model outperforms the traditional K‐means method with regards to the enhanced features using various auto‐encoder, pre‐trained weight and principle component analysis (PCA).

The consequence of road accidents that involves a motorcycle is far more fatal for the rider than the other drivers. Yet, there has not been an effective vehicle alert system that can eliminate these avoidable motorcycle accidents caused by other drivers where they fail to notice the motorcycles. One of the major flaws with the existing vehicle alert systems is that it should not treat motorcycles as same as other vehicles as they take much longer time to brake than a cars do. Therefore, this project aimed to find an effective method to identify motorcycles and alert the other drivers when motorcyclists are around them in 20-meter radius. After extensive literature review, the best method to solve the problem is to use road side infrastructure based Internet of Things (IOT) that divides the network into a set of clusters. In this method to identify a vehicle, it is identifying the driver and the rider from their smartphone application that beacons custom, unique Media Access Control (MAC) addresses via Bluetooth or Wi-Fi to the IOT probes. The probe differentiates the users, registers them when they arrive into the network, alerts the driver about motorcycles around them and removes them from the database when they move to other cluster. The whole scenario is simulated using the OMNET++ simulator and INET framework to demonstrate how the methodology works. If the concept is implemented in real-life, many valuable lives of motorists will be much safer on the road.

Robust vessel segmentation of fundus images is of great interest for better diagnosis of many diseases like diabetic retinopathy, retinopathy of prematurity, vein occlusions and so on. In this paper, we propose a novel example-based vessel segmentation method, based on learning the mapping relationship between fundus images and their corresponding ground truths. Firstly, the training images and their corresponding ground truths are divided into patches and clustered. Secondly, the mapping functions for each cluster are computed in a simple and efficient way from the training patches to their manual segmentation patches. Finally, Vessel segmentation are reconstructed by the simple mapping functions. Experimental results show that our method is efficient and can achieve competitive performance for vessel segmentation problems.

Effective forecasting of future prevalent topics plays animportant role in social network business development.It involves two challenging aspects: predicting whethera topic will become prevalent, and when. This cannotbe directly handled by the existing algorithms in topicmodeling, item recommendation and action forecasting.The classic forecasting framework based on time seriesmodels may be able to predict a hot topic when a seriesof periodical changes to user-addressed frequency in asystematic way. However, the frequency of topics discussedby users often changes irregularly in social networks.In this paper, a generic probabilistic frameworkis proposed for hot topic prediction, and machine learningmethods are explored to predict hot topic patterns.Two effective models, PreWHether and PreWHen, areintroduced to predict whether and when a topic will becomeprevalent. In the PreWHether model, we simulatethe constructed features of previously observed frequencychanges for better prediction. In the PreWHen model,distributions of time intervals associated with the emergenceto prevalence of a topic are modeled. Extensiveexperiments on real datasets demonstrate that ourmethod outperforms the baselines and generates moreeffective predictions.

Dynamic power management (DPM) is a power dissipation reduction technology aimed to adapting the power and performance of a system to its workload. In this paper, we propose a gradient learning optimization method for the DPM problem. Our method does not depend on accurate model parameters and is only based on a single sample path of system. Thus, there is no any transition probability to be calculated. Moreover, the new method only need less storage for the performance optimization. Simulation results demonstrate the applicability of the proposed method.

The topic of spam filtering has been thoroughly studied by researchers in the past few decades. There has been successful works with high spam detection rates, yet no paper has described a method which can effectively detect spam and, at the same time, measure the importance of ham emails. In this paper, the authors propose a method of generating SpamAssassin rules which can indicate the degree of importance of an email message. Specifically we added a proportion of negatively weighted ham rules and adapted HPSOWM, an efficient evolutionary algorithm, to optimize SpamAssassin rule scores. As a result, using our new rule set, SpamAssassin is able to give indicative scores for both spam and ham. These scores can be utilized by email clients to categorize incoming messages based on their importance to user. Various experiments were conducted to evaluate our method. In addition, a conclusion was drawn about the best ratio of spam rules and ham rules.

In this paper, a self-provisioning of communication network service based on a fuzzy particle swarm optimization is proposed to minimize the configuration cost of four layer communication network. An swarm optimization called fuzzy particle swarm optimization (FPSO) is introduced. In this FPSO, the inertia weight of PSO is adaptively determined by a set of fuzzy rule. Also, a cross-mutated operation is presented to drive the solution to escape from local optima where the control parameter of this operation is also governed by a set of fuzzy rule. A performance comparison is given to show the performance of the proposed FPSO on the self-provisioning of communication network service and found that the performance of FPSO is significantly better than that of the existing hybrid PSO methods in a statistical sense.

Multiple targets tracking is a popular application with huge potentials in many practical areas, such as military air combat and civilian surveillance. Recent years, sensor networks, comprising of a large number of cheap, portable and tiny sensors, have attracted a lot of research interests in many disciplines. Alternative forms of sensors such as camera, can provide rich and vivid observation information. They have been widely applied into the environment monitoring or object surveillance. However, these devices are usually very expensive, especially, it becomes impractical to fulfill tasks cooperatively done within a group of such high cost devices. Recent work shows that despite the low information volume provided by the passive binary-detection based sensor, a group of such sensors can work together to achieve good target tracking performance. In this paper, we investigate a passive proximity binary sensor-based multiple target tracking system which can autonomically achieve the self-organized tracking capabilities without the intervention of human operators. The localization and tracking algorithm is achieve false alarm rates, robust under low detection probabilities and sensor ambiguity localization errors. Experimental results show promising performance in adopting this application in practice.

Immunology inspired computer security has attracted enormous attention as its potential impacts on the next generation service-oriented network operation system. In this paper, we propose a new agent-based threat awareness assessment strategy inspired by the human immune system to dynamically adapt against attacks. Specifically, this approach is based on the dynamic reconfiguration of the file access right for system calls or logs (e.g., file rewritability) with balanced adaptability and vulnerability. Based on an information-theoretic analysis on the coherently associations of adaptability, autonomy as well as vulnerability, a generic solution is suggested to break down their coherent links. The principle is to maximize context-situation awared systems' adaptability and reduce systems' vulnerability simultaneously. Experimental results show the efficiency of the proposed biological behaviour-inspired vulnerability awareness system.

Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients' blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients' blood glucose levels based on these patients' physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients' data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.

This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCM method performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods.

In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.

Future large-scale complex computing environments present challenges to the real-time intrusion detection systems (IDSs). In this paper, we design a prototype with hybrid software-enabled detection engine on the basis of our improved block-based neural network (BBNN), and integrate it with a high-frequency FPGA board to form a real-time intrusion detection system. The established prototype can seamlessly feed the large-scale NetFlow data obtained from Cisco routers directly into the improved BBNN based IDS. The corresponding BBNN structure and parameter settings have been improved and experimentally tested. Experimental performance comparisons have been conducted against four major schemes of Support Vector Machine (SVM) and Naive Bayes algorithm. The results show that the improved BBNN outperforms other algorithms with respect to the classification and detection performances. The false alarm rate is successfully reduced as low as 5.14% while the genuine detection rate 99.92% is still maintained.

In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed. The principle is to coordinate the results from each single-engine intrusion alert system, by seamlessly integrating with the multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process called HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and reduce the false positive rate significantly. The proposed HPSOWM especially works for the efficient classification of unknown behaviors and malicious attacks.

The limited power consumption, as a major constraint, presents challenges in improving the network throughput for Wireless Sensor Networks (WSNs). Due to the limited computational power, the applications of WSNs in Terrestrial Networks require the capability to pre-process the observation data so as to remove irrelevant features or factors from multi-dimensional dataset. This paper proposes a intelligent distributed energy efficient routing algorithm inspired from natural learning and adaptation process with the aid of hybrid Neural Rough Sets theory, which is used to efficiently reduce the dimensionality of input dataset. The algorithmic implementation and experimental validation are described in this paper. Details of the algorithm and its testing procedures are presented in comparison with the other power-aware protocols, e.g., mini-hop. The validation of the proposed model is carried out via a wireless sensor network test-bed implemented in Castalia Simulator. The experimental results show the network performance measurements such as delay, throughput and packet loss that have been greatly improved as the outcome of applying this integration with Neural Rough Sets.

A fuzzy particle swarm optimization (PSO) for the minimization of makespan in permutation flow shop scheduling problem is presented in this paper. In the proposed fuzzy PSO, the inertia weight of PSO and the control parameter of the cross mutated operation are determined by a set of fuzzy rules. To escape the local optimum, cross-mutated operation is introduced. In order to make PSO suitable for solving permutation flow shop scheduling problem, a roulette wheel mechanism is proposed to convert the continuous position values of particles to job per mutations. Meanwhile, a swap-based local search for scheduling problem is designed for the local exploration on a discrete job permutation space. Flow shop benchmark functions are employed to evaluate the performance of the fuzzy PSO for flow shop scheduling problems and the results indicate that the algorithm performs better compared with existing hybrid PSO algorithms.

This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.