Current generative technologies have enabled the creation of realistic synthetic media, including face images. Although such synthetic data may pose a threat when used for malicious purposes, e.g., the spread of disinformation or impersonation, these data can be leveraged to improve the performance of Face Recognition Systems (FRS). This paper then proposes a strategy that leverages synthetic face images to reduce biases in FRS; in other words, to reduce unexpected and incorrect results due to imbalances in the training data in terms of ethnicity, gender, and other human traits. The proposed strategy balances the samples available for training with synthetic images, which are used exclusively during an initial training process. Once such an initial training process is complete, our strategy resumes training on the reference dataset (real data) to learn the target identities. This strategy has two main advantages: (1) it can bias the model towards a specific group of people to which a person of interest belongs, and (2) it can increase the authentication performance for a person of interest by boosting specificity. Our experiments on several datasets, including one for crime suspect authentication, confirm the advantages of our strategy.

Decision-making in real-world settings rarely follows a fixed script. Instead, it unfolds as a dynamic reasoning process in which the appropriate course of action evolves as new context and data become available. Traditional Business Process Management systems provide rigor, determinism, and auditability, yet they generally struggle to adapt their execution at runtime. Conversely, agentic systems based on Large Language Models (LLMs) bring flexibility to decision-making, but they are inherently opaque, often unreliable, and suffer from significant scalability constraints when operating over large datasets. To combine these complementary paradigms, we introduce VADAOrchestra, a neurosymbolic framework that models complex workflows as evolving reasoning processes. The framework adopts a hybrid approach: given a user query and a collection of data sources, an LLM-based orchestrator incrementally plans and adapts the workflow. This is encoded as a logic program in a fragment of Datalog+/- where predicates correspond to tool invocations and rules represent both predefined domain dependencies and logic constructs synthesized on demand to manipulate intermediate results. All logical inference tasks are then executed by a state-of-the-art Datalog+/- symbolic engine. This approach provides a verifiable reasoning trace, supporting the auditability and reproducibility of the entire process. Furthermore, by decoupling high-level orchestration from symbolic inference, it addresses scalability concerns, enabling complex reasoning over large datasets through targeted data querying. We evaluate VADAOrchestra on real-world financial use cases, demonstrating faithfulness, scalability, and explainability compared to standard agentic architectures.

The increasing adoption of Extended Reality (XR) technologies brings immersive interfaces into critical domains like healthcare and manufacturing. However, deciding how to protect users in these environments remains an open challenge. Although prior research explores individual authentication mechanisms, existing selection methods ignore context-specific constraints, environmental factors, and user perceptions central to XR. To address this, we conducted a qualitative study with usable-security experts to uncover key design considerations that current approaches overlook. Next, we mapped well-known selection methodologies against these considerations and identified important mismatches. In response, we developed AURA-XR, a risk-based framework integrating stakeholder perceptions, environmental and scenario-specific constraints, and risk assessment into a decision model. We demonstrate that AURA-XR supports context-sensitive, multi-objective authentication decisions tailored to this emerging domain. By filling a methodological gap, AURA-XR advances adaptive, privacy-aware, human-centred security in immersive systems, opening new routes for robust, situationally informed authentication in XR.

Security Operations Centres (SOCs) face sustained pressure from alert fatigue, fragmented tooling, analyst cognitive overload, and increasingly complex multi-stage attack campaigns. Although Artificial Intelligence (AI) and Machine Learning (ML) can support detection, triage, and response, their operational value is constrained when trust, transparency, accountability, and human oversight are not systematically addressed. This paper presents the Adaptive Trust-Aware SOC Human–AI Teaming (ATA-SOC-HAT) framework, a design-oriented conceptual framework for trust-calibrated collaboration between SOC analysts and AI services. The framework was derived from a structured literature review and maps recurrent barriers in SOC human–AI collaboration to explicit functional modules, including explainable interaction, dynamic task allocation, trust calibration, arbitration, and continuous learning. We describe the framework architecture and workflow and illustrate its intended operation through an advanced threat scenario. Rather than claiming empirical validation, the paper provides a prioritised evaluation roadmap that identifies a practical core set of metrics for future practitioner-in-the-loop studies, simulations, and prototype implementations. We also summarise how the framework aligns with relevant cybersecurity and trustworthy-AI guidance to support accountable deployment. The contribution of this work is therefore a traceable, literature-derived conceptual framework and a realistic basis for future empirical evaluation of human–AI teaming in SOC environments.

In an era characterised by evolving cyber threats and sophisticated adversar-ial behaviour, the field of cyber-security faces a continuous and formidablechallenge. The development of dynamic and adaptive security control mea-sures is imperative in order to safeguard critical assets and information.This article delves into the realm of Moving Target Defence (MTD), astrategic approach that seeks to outmanoeuvre adversaries by constantlyshifting the security landscape. Our research specifically focuses on the ap-plication of Reinforcement Learning (RL) in MTD, with a focus on threatexposure and the efficacy of control strategies with respect to risk reduction.A defensive RL agent is proposed that incorporates attack graphs as ablueprint to assess possible paths that an adversary may take. By consideringreceived events about an adversary’s actions, the defensive agent continuouslyupdates its knowledge about the adversary’s position on the attack graph.The proposed research establishes and evaluates a risk-based, RL-drivenagent capable of MTD operations in order to address adversarial behaviours.The proposed approach provides valuable insights into optimally deployingsecurity controls under dynamic threat scenarios and restricted budget resources.

Intelligent transportation systems integrate Internet of Things, blockchain, and quantum-resistant cryptography to enhance autonomous vehicle operations, urban mobility, and road safety. However, their reliance on heterogeneous communication networks exposes them to cyber threats, including identity spoofing, data tampering, and quantum-enabled attacks that compromise security and privacy. To address these challenges, this paper proposes a blind quantum computation-enhanced identity-based quantum-secure framework, which combines decentralized authentication, blockchain-based identity verification, and post-quantum cryptographic techniques to reduce long-term trust in third parties by eliminating persistent key escrow, while mitigating security risks. The framework leverages quantum spin-state mapping and blind quantum encryption to protect against quantum-enabled collision (birthday-type) and key impersonation attacks under the defined adversary model, ensuring tamper-proof and unlinkable transactions in vehicular networks. Additionally, a lightweight consensus mechanism optimizes computational efficiency while maintaining high security and scalability. Through private-chain simulations and micro-benchmarks, Identity-Based Quantum Signature achieves ~94.5 s confirmation time for a batch of 600 transactions (≈ 6.3 TPS) in off-path audit and enrollment functions, while maintaining lightweight cryptographic operations suitable for real-time V2V safety messages (signing ≈ 2.994 ms, verification ≈ 1.493 ms). These results position IBQS as a practical and quantum-resilient security solution for next-generation decentralized transportation systems.

Differential privacy (DP) is the leading standard for privacy protection, providing rigorous privacy guarantees for various data. However, its conventional approach of treating all records uniformly regarding privacy risk and using a non-adaptive privacy budget ($\epsilon$) often compromises data utility in subsequent analyses. This uniform treatment and fixed$\epsilon$can introduce significant perturbations, making the secondary use of shared data challenging. To overcome these limitations, we introduce a novel record-sensitivity-aware and Particle Swarm Optimization (PSO)-driven customised$\epsilon$-DP method for data perturbation. Our approach significantly enhances data utility without compromising privacy in data sharing by introducing three key optimisations to the traditional DP framework: First, we partition records into three sensitivity classes (high, medium, and low) based on the privacy risk. Second, we adopt a PSO mechanism to determine the optimal$\epsilon$for each partition, perturbing data with a variable$\epsilon$that considers sensitivity, rather than using a single, fixed$\epsilon$for the entire dataset. Finally, noise is injected by grouping attributes horizontally, rather than adding noise to each attribute independently, to prevent the generation of inconsistent values in the perturbed data. Detailed experiments on real benchmark and synthetic datasets demonstrate the superiority of our method in terms of utility and privacy across seven evaluation metrics, compared to the latest state-of-the-art$\epsilon$-DP methods.

Financial sentiment analysis is the task of evaluating and quantifying the emotions and opinions expressed in financial news, reports, or social media to help investors and institutions make informed decisions. Financial institutions have been actively exploring the use of large language models (LLMs) to analyse market sentiment signals for a more nuanced understanding of a broader context. However, issues such as the scale of training data, model complexity, and the potential for human oversight can introduce or even amplify bias in these systems. Representation bias is a common challenge for LLMs as training data fail to properly represent the target groups, hence causes harmful bias in general-purpose use. Therefore, replacing current solutions with LLMs in financial organisations requires a robust evaluation methodology to ensure fairness. This paper investigates a three-level bias evaluation approach that specifically focuses on representation bias and presents a baseline evaluation of the FinBERT model. Step 1 uses a synthetic dataset that explicitly reveals sources of bias, structured as probability- and embedding-based evaluation recipes. Step 2 evaluates the model against data released by another country (e.g. Indian News dataset) to assess its performance in relation to more implicit biases. Step 3 examines individual problematic samples using token-based interpretability methods (e.g. integrated gradients). This paper presents the application of this structured bias evaluation process and its results on the FinBERT model. The evaluation code and dataset are available on GitHub (https://github.com/asabuncuoglu13/faid-test-financial-sentiment-analysis).

The widespread adoption of EMV (Europay, Mastercard, and Visa) contactless payment systems has greatly improved convenience for both users and merchants. However, this growth has also exposed significant security challenges. This SoK provides a comprehensive analysis of security vulnerabilities in EMV contactless payments, particularly within the open-loop systems used by Visa and Mastercard. We categorize attacks into seven attack vectors across three key areas: application selection, cardholder authentication, and transaction authorization. We replicate the attacks on Visa and Mastercard protocols using our experimental platform to determine their practical feasibility and offer insights into the current security landscape of contactless payments. Our study also includes a detailed evaluation of the underlying protocols, along with a comparative analysis of Visa and Mastercard, highlighting vulnerabilities and recommending countermeasures.

Smartphones balance security and convenience by offering both knowledge-based (PINs, patterns) and biometric (facial, fingerprint) verification methods. However, studies have reported that PINs and patterns can be readily circumvented, while synthetically manipulated face data can easily deceive smartphone facial verification mechanisms. In this paper, we design a bimodal user verification mechanism that combines behavioral (pickup gesture) and biological (face) biometrics for user verification on smartphones. This work establishes a baseline for single-user verification scenarios on smartphones using a one-class verification model. The evaluation is performed in two stages: first, performance is assessed in both unimodal and bimodal settings using publicly available datasets; second, the robustness of the employed biological and behavioral traits is examined against four diverse attacks. Our findings emphasize the necessity of investigating diverse attack vectors, particularly fully synthetic data, to design robust user verification mechanisms.

Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while restricting the movement of data. Since FL provides participants with independence over their training data, it becomes susceptible to poisoning attacks. Such collaboration also propagates bias among the participants, even unintentionally, due to different data distribution or historical bias present in the data. This paper proposes an intentional fairness attack, where a client maliciously sends a biased model, by increasing the fairness loss while training, even considering homogeneous data distribution. The fairness loss is calculated by solving an optimization problem for fairness metrics such as demographic parity and equalized odds. The attack is insidious and hard to detect, as it maintains global accuracy even after increasing the bias. We evaluate our attack against the state-of-the-art Byzantine-robust and fairness-aware aggregation schemes over different datasets, in various settings. The empirical results demonstrate the attack efficacy by increasing the bias up to 90%, even in the presence of a single malicious client in the FL system.

All-but-one Vector Commitments (AVCs) randomly opens all but one of the committed vector values. Typically AVCs are instantiated using Goldwasser-Goldreich-Micali (GGM) trees. Generating these trees comprises a significant computational cost for AVCs due to a large number of hash function calls. Correlated GGM (cGGM) trees have been proposed to halve the number of hash calls and Batched AVCs (BAVCs) using a single GGM tree were integrated in the FAEST signature scheme, which improves efficiency and reduces the signature sizes. This paper proposes BACON, a BAVC with aborts that leverages a single cGGM tree. BACON executes multiple instances of AVC in a single batch and enables an abort mechanism to probabilistically reduce the commitment size. We prove that BACON is secure under the ideal cipher model and the random oracle model. We also discuss the possible application of the proposed BACON and show the theoretical efficiency compared to state-of-the-art.

Authentication in extended reality (XR) presents unique challenges due to embodied interaction, spatial immersion, and variable environmental conditions. As XR systems become more prevalent, secure and usable authentication mechanisms are critical. However, current research often overlooks the scenarios in which these mechanisms operate, limiting comparability, reproducibility, and real-world applicability. This paper addresses this gap by presenting a structured model of XR authentication scenarios. We conducted semi-structured interviews with experts in the Usable Security and Privacy domain to identify key scenario dimensions influencing the design and evaluation of XR authentication mechanisms. Through thematic analysis, we identified dimensions related to contextual parameters, environmental conditions, and XR-specific properties. The resulting scenario model was validated through literature mapping and demonstrated via a realistic use case. Our work provides a foundation for context-aware design and more rigorous evaluation of authentication mechanisms across diverse XR environments.

Additive Manufacturing (AM) is transforming industries by enabling rapid prototyping and customised production. However, as AM processes become increasingly digitised and interconnected, they introduce significant cybersecurity vulnerabilities, including intellectual property theft, design manipulation, and counterfeit production. This paper offers a comprehensive analysis of cyber and cyber–physical threats within the AM supply chain, addressing a critical research gap that has largely focused on isolated security aspects. Building upon existing taxonomies, we expand cybersecurity frameworks to incorporate emerging AM-specific threats. We propose a structured attack taxonomy that categorises threats by attacker goals, targets, and methods, supported by real-world case studies. The paper emphasizes the need for robust cybersecurity measures to protect intellectual property, ensure production integrity, and strengthen supply chain security. Finally, we present mitigation strategies to counter these threats, laying the foundation for future research and best practices to secure AM ecosystems.

Abstract This article examines the impact of utilizing generative artificial intelligence optimizations in automating the content generation process. This instance involves the identification of fraudulent content, which is often characterized by dynamic patterns, in addition to content production. The generated contents are constrained, which limits their dimensionality. In this scenario, duplicated contents are eliminated from the automatic creations. Furthermore, the generated ratios are utilized to discover current patterns with minimized losses and errors, hence enhancing the accuracy of generative contents. Furthermore, while analysing the created patterns, we detect a significant discrepancy in lead durations, resulting in the generation of high scores for relevant information. In order to test the results using generative tools, the adversarial network codes are employed in four scenarios. These scenarios involve generating large patterns and reducing the dynamic patterns with an enhanced accuracy of 97% in the projected model. This is in contrast to the existing approach, which only provides a content accuracy of 77% after detecting fraud.

In sharded blockchains, peers are divided into smaller groups (shards) that generate and verify blocks in parallel, offering enhanced throughput and reduced delays. These properties make sharded blockchains a promising solution for secure data management in Internet of Things (IoT) systems. Particularly, geolocation-based sharded blockchains assign geographically proximate peers to the same shard, enabling faster IoT transaction processing. Yet, peers in each shard can easily collude to falsely accept/reject blocks. To resolve this issue, in this paper, we propose a robust reputation-based shard inspection framework. The framework adopts the shard inspection mechanism where a group of inspectors selected from the most reputable peers randomly verify blocks in each shard. This enables avoiding collusion attacks and enhancing the security of each shard. However, additional block verifications during the inspection process can incur significant block delays and energy overheads. To reduce these overheads, we formulate an optimization problem that jointly determines the number of inspectors and the inspection interval to maximize the system utility, which is proportional to the blockchain throughput and energy consumption. We then develop a distributed algorithm that enables dividing the optimization problem into sub-problems solvable independently by each shard. Experimental results show that our framework can maximize the system utility, while maintaining high levels of security in each shard.

The Industrial Internet of Things (IIoT) has revolutionized industrial automation, enabling real-time monitoring and intelligent decision-making. However, the increasing connectivity of IIoT devices exposes them to cyber threats, necessitating robust Intrusion Detection Systems (IDS). Traditional centralized IDS solutions face concerns regarding sharing of sensitive data, high computational costs, and communication overhead. Federated Learning (FL) provides a privacypreserving alternative to such centralized systems. However, existing FL-based IDS frameworks may face challenges such as high resource consumption and privacy threats such as gradient leakage from shared updates. To address these challenges, we propose Robust Optimization for Encrypted Federated Learning (ROCHE), a lightweight FL-based IDS optimized for IIoT, ensures data privacy and efficiency. ROCHE uses low-degree polynomial approximations to replace complex activation functions, reducing computational load without significantly impacting accuracy. An adaptive quantization mechanism is utilized to reduce bandwidth consumption while ensuring accurate model convergence. To preserve data privacy during model aggregation, ROCHE integrates symmetric homomorphic encryption, enabling secure model updates while maintaining resilience to user dropout. Comprehensive security analysis and experiments demonstrate that ROCHE outperforms state-of-the-art frameworks. Compared to MiTFed, ROCHE reduces computational overhead by 11% and lowers communication cost by 16%, demonstrating its efficiency in optimizing resource utilization while maintaining robust privacy preservation in FL based IDS. Additionally, ROCHE maintains an average accuracy of over 90% across multiple attack types. Deployment in a cloud-based IIoT environment demonstrates its feasibility, establishing ROCHE as a scalable and efficient IDS for IIoT security.

The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. To address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. To the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model.

Vehicle platooning enables vehicles to drive cooperatively on highways and motorways. This is to weigh numerous benefits such as lesser fuel consumption, safe drive and better road utilization. The main research focus of vehicle platoons is secure and dynamic platoon formation and ensuring privacy of vehicular data. Platoons are not safe from cyber-attacks and key is to safeguard the platoons from different known/unknown cyber threats. This paper introduces a dynamic and secure platoon formation technique targeting three different vehicle conditions on road. While the private credentials of CAVs is protected using zk-SNARK encryption protocol through permissioned Blockchain. The proposed system has been evaluated for its performance against DDoS and impersonation attack. Platoon formation time has been compared with other benchmarks and shows better results. While the performance against DDoS and impersonation attacks in comparison to the benchmarks is also improved. Since the limitation of zk-SNARK is it takes time to generate a proof and this has been also the focus of this study. The protocol has been fine tuned to adjust its parameters so that it could generate proof in less possible time.

Advancements in 6G communication technology hold great promise for Vehicular Ad Hoc Networks (VANETs), providing critical real-time data to vehicles with low latency, high speed, and enhanced network capacity. While 6G communications significantly improve transportation efficiency, they also introduce significant challenges in securing communication. Several studies have addressed anonymous authentication and key exchange protocols for 6G-enabled VANETs, including the work (doi.org/10.1109/TITS.2021.3099488) by Vijayakumar et al. on Anonymous Batch Authentication and Key Exchange Protocols. Although this protocol is claimed to be secure, our analysis identifies critical vulnerabilities, including susceptibility to man-in-the-middle attacks, impersonation, and unauthorized access. This paper proposes a secure and anonymous batch authentication and key exchange protocol for 6G-enabled VANETs to address this challenge. The formal security analysis demonstrates that the proposed solution effectively mitigates man-in-the-middle attacks, impersonation, and unauthorized access. Additionally, performance evaluations show that the proposed scheme is practically feasible.

With the gradual increase in market share of electric vehicle (EV), Vehicle to Grid(V2G) has become a new research hotspot in the field of intelligent transportation systems. Its goal is to avoid overloading the power grid due to the simultaneous charging of a large number of electric vehicles. However, when EV is connected to the power grid, V2G will involve a large amount of privacy data exchange. Once these data are leaked, the privacy and security of users will be threatened. Ensuring the secure transmission of user privacy information in V2G is crucial. Therefore, this paper proposes a practical and secure authentication protocol for V2G in intelligent transportation systems. This protocol ensures user login security through three-factor authentication mechanism and then implements authentication based on Chebyshev chaotic maps. Finally, secure communication is carried out through the established key. Security analysis shows that this protocol is secure and can ensure the privacy and security of V2G. Informal security analysis shows that this protocol can meet various security attributes. Functional comparison and performance analysis indicate that the protocol not only has high security but also has low computation and communication overhead.

Machine learning models have been instrumental in making decisions across domains, like mortgage lending and risk assessment in finance. However, these models have been found susceptible to biases, causing unfair decisions for a specific group of individuals. Such bias is generally based on some protected (or sensitive) attributes, such as age, sex, or race, and is still prevalent due to historical context or algorithmic bias. There have been several efforts to ensure equal opportunities for each individual/group, based on creditworthiness, rather than any social bias. Several pre-, in- and post-processing bias mitigation techniques have been proposed. However, these techniques perform data transformation or design new constraint/cost functions, which are task-specific, to achieve a fair prediction. Such techniques even require further access to the complete training/testing data. This paper proposes a novel post-processing bias mitigation technique that employs a model interpretation strategy to find the responsible model weights causing the bias. Pruning only a few model weights exhibits group fairness in model predictions while maintaining competitive accuracy levels, thus aligning with the goals of fairness and efficiency in decision-making. The proposed scheme requires access to only a few data samples representing the protected attributes, without exposing the complete training data. Through extensive experiments with multiple census datasets/methods, we demonstrate the efficacy of our approach, achieving up to a significant 50% reduction in bias while preserving the overall accuracy.

Denoising diffusion generative models are state-of-the-art methods for generating synthetic images that have also proved successful in tabular and graph synthetic data generation. However, their computational complexity has limited the application of these techniques to graph data, focusing usually on smaller graphs, such as those used in molecular modeling. In this paper, we propose SaGess, a discrete denoising diffusion approach, which is able to generate large real-world networks. Through a generalized divide-and-conquer framework, SaGess overcomes the scaling limitations of the diffusion model DiGress, by sampling a covering of subgraphs of the initial graph, training a DiGress module, and finally reconstructing a synthetic graph using the subgraphs that have been generated using the DiGress module. We evaluate the quality of the synthetic data sets against several competitor methods by comparing graph statistics between the original and synthetic samples, as well as evaluating the utility of the synthetic data set produced by using it to train a task-driven model, namely link prediction. In our experiments, SaGess outperforms most of the one-shot state-of-the-art graph generating methods by a significant factor, both on the graph metrics and on the link prediction task.

Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques have been devised under differential privacy, they predominantly assume data is centralized. However, data is often distributed across multiple clients in a federated manner. In this work, we initiate the study of federated synthetic tabular data generation. Building upon a SOTA central method known as AIM, we present DistAIM and FLAIM. We first show that it is straightforward to distribute AIM, extending a recent approach based on secure multi-party computation which necessitates additional overhead, making it less suited to federated scenarios. We then demonstrate that naively federating AIM can lead to substantial degradation in utility under the presence of heterogeneity. To mitigate both issues, we propose an augmented FLAIM approach that maintains a private proxy of heterogeneity. We simulate our methods across a range of benchmark datasets under different degrees of heterogeneity and show we can improve utility while reducing overhead.

Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release of weights, weight stealing and fine-tuning APIs make closed models vulnerable to harmful fine-tuning attacks (HFAs). While safety measures like preventing jailbreaks and improving safety guardrails are important, such measures can easily be reversed through fine-tuning. In this work, we propose Representation Noising (\textsf{\small RepNoise}), a defence mechanism that operates even when attackers have access to the weights. \textsf{\small RepNoise} works by removing information about harmful representations such that it is difficult to recover them during fine-tuning. Importantly, our defence is also able to generalize across different subsets of harm that have not been seen during the defence process as long as they are drawn from the same distribution of the attack set. Our method does not degrade the general capability of LLMs and retains the ability to train the model on harmless tasks. We provide empirical evidence that the efficacy of our defence lies in its ``depth'': the degree to which information about harmful representations is removed across {\em all layers} of the LLM. We also find areas where \textsf{\small RepNoise} still remains ineffective and highlight how those limitations can inform future research.

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

Face image synthesis detection is considerably gaining attention because of the potential negative impact on society that this type of synthetic data brings. In this paper, we propose a data-agnostic solution to detect the face image synthesis process. Specifically, our solution is based on an anomaly detection framework that requires only real data to learn the inference process. It is therefore data-agnostic in the sense that it requires no synthetic face images. The solution uses the posterior probability with respect to the reference data to determine if new samples are synthetic or not. Our evaluation results using different synthesizers show that our solution is very competitive against the state-of-the-art, which requires synthetic data for training.

The proliferation of multimedia-enabled IoT devices and edge computing enables a new class of data-intensive applications. However, analyzing the massive volumes of multimedia data presents significant privacy challenges. We propose a novel framework called generative adversarial privacy (GAP) that leverages generative adversarial networks (GANs) to synthesize privacy-preserving surrogate data for multimedia analytics across the IoT-Edge continuum. GAP carefully perturbs the GAN's training process to provide rigorous differential privacy guarantees without compromising utility. Moreover, we present optimization strategies, including dynamic privacy budget allocation, adaptive gradient clipping, and weight clustering to improve convergence and data quality under a constrained privacy budget. Theoretical analysis proves that GAP provides rigorous privacy protections while enabling high-fidelity analytics. Extensive experiments on real-world multimedia datasets demonstrate that GAP outperforms existing methods, producing high-quality synthetic data for privacy-preserving multimedia processing in diverse IoT-Edge applications.

Autonomous robotic systems systems are both safety- and security-critical, since a breach in system security may impact safety. In such critical systems, formal verification is used to model the system and verify that it obeys specific functional and safety properties. Independently, threat modelling is used to analyse and manage the cyber security threats that such systems may encounter. Both verification and threat analysis serve the purpose of ensuring that the system will be reliable, albeit from differing perspectives. In prior work, we argued that these analyses should be used to inform one another and, in this paper, we extend our previously defined methodology for security-minded verification by incorporating runtime verification. To illustrate our approach, we analyse an algorithm for sending Cooperative Awareness Messages between autonomous vehicles. Our analysis centres on identifying STRIDE security threats. We show how these can be formalised, and subsequently verified, using a combination of formal tools for static aspects, namely Promela/SPIN and Dafny, and generate runtime monitors for dynamic verification. Our approach allows us to focus our verification effort on those security properties that are particularly important and to consider safety and security in tandem, both statically and at runtime.

Strategic investment(s) in vehicle automation technologies led to the rapid development of technology that revolutionised transport services and reduced fatalities on a scale never seen before. Technological advancements and their integration in Connected Autonomous Vehicles (CAVs) increased uptake and adoption and pushed firmly for the development of highly supportive legal and regulatory and testing environments. However, systemic threats to the security and privacy of technologies and lack of data transparency have created a dynamic threat landscape within which the establishment and verification of security and privacy requirements proved to be an arduous task. In CAVs security and privacy issues can affect the resilience of these systems and hinder the safety of the passengers. Existing research efforts have been placed to investigate the security issues in CAVs and propose solutions across the whole spectrum of cyber resilience. This paper examines the state-of-the-art in security and privacy solutions for CAVs. It investigates their integration challenges, drawbacks and efficiencies when coupled with distributed technologies such as Blockchain. It has also listed different cyber-attacks being investigated while designing security and privacy mechanism for CAVs.

As a component of the Intelligent Transportation System (ITS), Internet of Vehicles (IoV) is becoming increasingly important in the management and construction of urban transportation as it can provide users with a range of applications related to traffic accident warnings, entertainment information, collaborative driving and real-time road information through communication devices on vehicles. However, with the increasing variety of services in the IoV, the growing demand for user traffic and the advances in Unmanned Aerial Vehicle (UAV) technology, UAV is introduced into the IoV as a solution, which can relieve the pressure on the communication infrastructure in the network, provide emergency communication services and improve the performance of network services. Due to the openness of IoV and the high-speed movement of vehicles, authentication and privacy issues are among the most pressing issues in IoV. Therefore, the paper proposes a secure and effective authentication protocol for UAV-assisted IoV. The protocol utilises elliptic curve cryptography to assure the security of the authentication. The protocol undergoes proof of security, Burrows-Abadi-Needham (BAN) logic analysis and informal security analysis to ensure secure and mutual authentication, and have a good resistance to known attacks. Furthermore, performance analysis and comparison are conducted to evaluate the efficiency of our protocol. The results indicate that our protocol has superior advantages in overhead.

In this paper, a realization technique is designed with a unique analytical model for transmitting multimedia data to appropriate end users. Transmission of multimedia data to all end users through a variety of visualization methods is the foundation of future computer systems. Yet, highly limited system resources prevent the updating of the methods used to manage multimedia data. Hence, a high-end visualization technique where uncertainties are eliminated is required for the visualization process with a multimedia system. As a result, the suggested system incorporates a clustering technique utilizing an analytical framework to ensure a high degree of transmission for all multimedia data. The technical contribution of the proposed method depends on a multimedia visualization process that takes place with high security features by including necessary parametric relationships such as occurrence of jitter, data density points, time period, multimedia storage, data smoothness and distance. For the established parametric relationship the validation methodology is integrated with a hierarchical clustering algorithm, thereby transmitting every clustered data with high security feature, thereby the examined outcomes under five scenarios proves that data security which is represented by simulation outcomes is improved to 88% as compared to the existing approach.

Consortium blockchains have attracted considerable interest from academia and industry due to their low-cost installation and maintenance. However, typical consortium blockchains can be easily attacked by colluding block validators because of the limited number of miners in the systems. To address this problem, in this paper, we propose a novel block validation framework to enhance blockchain security. In the framework, the block validations are assisted and implemented by various lightweight nodes, e.g., edge devices, in addition to the typical blockchain miners. This improves the blockchain security but can cause an increased block validation delay and, thereby, reduced blockchain throughput. To tackle this challenge, we propose an effective method to select lightweight nodes based on their computing powers to maximize the blockchain throughput, and prove the uniqueness of the optimal nodes selection strategy. Security analysis and simulation results from the deployed consortium blockchain platform show that the proposed framework achieves higher throughput and security than the existing consortium blockchain models.

Financial institutions collect, control and process sensitive data, either personally or commercially. These data are vital for many purposes, including business process optimisation, tailored services to individuals and organisations, and preventing fraud or other misuse of the financial system. While an institution’s information is valuable, its power is amplified when combined with other data stored in different organisations horizontally, vertically or in a governance role. However, sharing data between organisations becomes difficult for commercial, legal and regulatory reasons. Privacy-enhancing Technologies (PETs) are becoming a practical solution for secure and private information sharing. One such PET, Homomorphic Encryption (HE), allows the computation over encrypted data and thus protects the confidentiality of the information while increasing its utility. This paper investigates the current position regarding the use of HE in the financial sector by analysing the LexisNexis news archive using statistical instruments on NLP and ML techniques. Our results show that the financial sector has gained momentum in discussing privacy-enhancing technology over the last few years. The discussion around this new field highlights a constantly growing match in capitalising market readiness to technology readiness.

The growing integration of high-wattage Internet of Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This article introduces a novel approach-based a multioutput network (2-D convolutional neural networks classifier and reconstruction decoder)—called “2DR-CNN”—to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14- and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine-learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.

The use of voice-controlled virtual assistants (VAs) is significant, and user numbers increase every year. Extensive use of VAs has provided the large, cash-rich technology companies who sell them with another way of consuming users’ data, providing a lucrative revenue stream. Whilst these companies are legally obliged to treat users’ information “fairly and responsibly,” artificial intelligence techniques used to process data have become incredibly sophisticated, leading to users’ concerns that a lack of clarity is making it hard to understand the nature and scope of data collection and use. There has been little work undertaken on a self-contained user awareness tool targeting VAs. PrivExtractor, a novel web-based awareness dashboard for VA users, intends to redress this imbalance of understanding between the data “processors” and the user. It aims to achieve this using the four largest VA vendors as a case study and providing a comparison function that examines the four companies’ privacy practices and their compliance with data protection law. As a result of this research, we conclude that the companies studied are largely compliant with the law, as expected. However, the user remains disadvantaged due to the ineffectiveness of current data regulation that does not oblige the companies to fully and transparently disclose how and when they use, share, or profit from the data. Furthermore, the software tool developed during the research is, we believe, the first that is capable of a comparative analysis of VA privacy with a visual demonstration to increase ease of understanding for the user.

Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). The data locality constraint offers strong privacy protection but also makes FL sensitive to the condition of local data. Apart from statistical heterogeneity, a large proportion of the clients, in many scenarios, are probably in possession of low-quality data that are biased, noisy or even irrelevant. As a result, they could significantly slow down the convergence of the global model we aim to build and also compromise its quality. In light of this, we first present a new view of local data by looking into the representation space and observing that they converge in distribution to Normal distributions before activation. We provide theoretical analysis to support our finding. Further, we proposeFedProf, a novel algorithm for optimizing FL over non-IID data of mixed quality. The key of our approach is a distributional representation profiling and matching scheme that uses the global model to dynamically profile data representations and allows for low-cost, lightweight representation matching. Using the scheme we sample clients adaptively in FL to mitigate the impact of low-quality data on the training process. We evaluated our solution with extensive experiments on different tasks and data conditions under various FL settings. The results demonstrate that the selective behavior of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2.4× speedup) for the global model to converge and also provides accuracy gain.

There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models can excel when privacy and communication constraints are imposed. Instead, tree-based approaches such as XGBoost have attracted much attention for their high performance and ease of use; in particular, they often achieve state-of-the-art results on tabular data. Consequently, several recent works have focused on translating Gradient Boosted Decision Tree (GBDT) models like XGBoost into federated settings, via cryptographic mechanisms such as Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). However, these do not always provide formal privacy guarantees, or consider the full range of hyperparameters and implementation settings. In this work, we implement the GBDT model under Differential Privacy (DP). We propose a general framework that captures and extends existing approaches for differentially private decision trees. Our framework of methods is tailored to the federated setting, and we show that with a careful choice of techniques it is possible to achieve very high utility while maintaining strong levels of privacy.

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations.

The increasing value of data held in enterprises makes it an attractive target to attackers. The increasing likelihood and impact of a cyber attack have highlighted the importance of effective cyber risk estimation. We propose two methods for modelling Value-at-Risk (VaR) which can be used for any time-series data. The first approach is based on Quantile Autoregression (QAR), which can estimate VaR for different quantiles, i. e. confidence levels. The second method, we term Competitive Quantile Autoregression (CQAR), dynamically re-estimates cyber risk as soon as new data becomes available. This method provides a theoretical guarantee that it asymptotically performs as well as any QAR at any time point in the future. We show that these methods can predict the size and inter-arrival time of cyber hacking breaches by running coverage tests. The proposed approaches allow to model a separate stochastic process for each significance level and therefore provide more flexibility compared to previously proposed techniques. We provide a fully reproducible code used for conducting the experiments.

The rise ofNewSpaceprovides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites,NewSpaceprovides the opportunity for delivering computing platforms in space. However, computational resources within space are usually expensive and satellites may not be able to compute all computational tasks locally. Computation offloading (CO), a popular practice in Edge/Fog computing, could prove effective in saving energy and time in this resource-limited space ecosystem. However, CO alters the threat and risk profile of the system. In this article, we analyze security issues in space systems and propose a security-aware algorithm for CO. Our method is based on the reinforcement learning technique, deep deterministic policy gradient (DDPG). We show, using Monte-Carlo simulations, that our algorithm is effective under a variety of environment and network conditions and provide novel insights into the challenge of optimized location of computation.

Advanced cyber attackers often “pivot” through several devices in such complex infrastructure to obfuscate their footprints and overcome connectivity restrictions. However, prior pivot attack detection strategies present concerning limitations. This paper addresses an improvement of cyber defence with APIVADS, a novel adaptive pivoting detection scheme based on traffic flows to determine cyber adversaries’ presence based on their pivoting behaviour in simple and complex interconnected networks. Additionally, APIVADS is agnostic regarding transport and application protocols. The scheme is optimized and tested to cover remotely connected locations beyond a corporate campus’s perimeters. The scheme considers a hybrid approach between decentralized host-based detection of pivot attacks and a centralized approach to aggregate the results to achieve scalability. Empirical results from our experiments show the proposed scheme is efficient and feasible. For example, a 98.54% detection accuracy near real-time is achievable by APIVADS differentiating ongoing pivot attacks from regular enterprise traffic as TLS, HTTPS, DNS and P2P over the internet.

Advances in communications, storage and computational technology allow significant quantities of data to be collected and processed by distributed devices. Combining the information from these endpoints can realize significant societal benefit but presents challenges in protecting the privacy of individuals, especially important in an increasingly regulated world. Differential privacy (DP) is a technique that provides a rigorous and provable privacy guarantee for aggregation and release. The Shuffle Model for DP has been introduced to overcome challenges regarding the accuracy of local-DP algorithms and the privacy risks of central-DP. In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model. The aim of this paper is twofold; first, we provide a single message protocol for the summation of real vectors in the Shuffle Model, using advanced composition results. Secondly, we provide an improvement on the bound on the error achieved through using this protocol through the implementation of a Discrete Fourier Transform, thereby minimizing the initial error at the expense of the loss in accuracy through the transformation itself. This work will further the exploration of more sophisticated structures such as matrices and higher-dimensional tensors in this context, both of which are reliant on the functionality of the vector case.

Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control—to name just a few—are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: personal information privacy, multi-party privacy, trust, and consent to share information. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. First, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Second, a large portion of time series data have complex seasonality features. Third, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency.

Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The dominant approach requires each user to randomly perturb their input, leading to guarantees in the local differential privacy model. In this paper, we place the various approaches that have been suggested into a common framework, and perform an extensive series of experiments to understand the tradeoffs between different implementation choices. Our conclusion is that for the core problems of frequency estimation and heavy hitter identification, careful choice of algorithms can lead to very effective solutions that scale to millions of users.

Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this article, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.

Modern network infrastructures host converged applications that demand rapid elasticity of services, increased security, and ultra-fast reaction times. The Tactile Internet promises to facilitate the delivery of these services while enabling new economies of scale for high fidelity of machine-to-machine and human-to-machine interactions. Unavoidably, critical mission systems served by the Tactile Internet manifest high demands not only for high speed and reliable communications but equally, the ability to rapidly identify and mitigate threats and vulnerabilities. This article proposes a novel Multi-Agent Data Exfiltration Detector Architecture (MADEX), inspired by the mechanisms and features present in the human immune system. MADEX seeks to identify data exfiltration activities performed by evasive and stealthy malware that hides malicious traffic from an infected host in low-latency networks. Our approach uses cross-network traffic information collected by agents to effectively identify unknown illicit connections by an operating system subverted. MADEX does not require prior knowledge of the characteristics or behavior of the malicious code or a dedicated access to a knowledge repository. We tested the performance of MADEX in terms of its capacity to handle real-time data and the sensitivity of our algorithm’s classification when exposed to malicious traffic. Experimental evaluation results show that MADEX achieved 99.97% sensitivity, 98.78% accuracy, and an error rate of 1.21% when compared to its best rivals. We created a second version of MADEX, called MADEX level 2, that further improves its overall performance with a slight increase in computational complexity. We argue for the suitability of MADEX level 1 in non-critical environments, while MADEX level 2 can be used to avoid data exfiltration in critical mission systems. To the best of our knowledge, this is the first article in the literature that addresses the detection of rootkits real-time in an agnostic way using an artificial immune system approach while it satisfies strict latency requirements.

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.

Source Location Privacy (SLP) is an important property for monitoring assets in privacy-critical sensor network and Internet of Things applications. Many SLP-aware routing techniques exist, with most striking a tradeoff between SLP and other key metrics such as energy (due to battery power). Typically, the number of messages sent has been used as a proxy for the energy consumed. Existing work (for SLP against a local attacker) does not consider the impact of sleeping via duty cycling to reduce the energy cost of an SLP-aware routing protocol. Therefore, two main challenges exist: (i) how to achieve a low duty cycle without loss of control messages that configure the SLP protocol and (ii) how to achieve high SLP without requiring a long time spent awake. In this article, we present a novel formalisation of a duty cycling protocol as a transformation process. Using derived transformation rules, we present the first duty cycling protocol for an SLP-aware routing protocol for a local eavesdropping attacker . Simulation results on grids demonstrate a duty cycle of 10%, while only increasing the capture ratio of the source by 3 percentage points, and testbed experiments on FlockLab demonstrate an 80% reduction in the average current draw.

This paper presents a machine learning approach to detect and classify misbehaviour in Vehicular Ad- hoc Networks. We describe three novel features obtained from analysis of n consecutive locations of a vehicle to form a judgement about its behaviour. These features are used in two machine learning algorithms (K-Nearest Neighbour and Support Vector Machine) for detecting attacks in the VeReMi dataset. We show that the overall precision rates can be as high as 99.7%, whilst the recall rates are consistently higher than 99%. The features we propose also help to reduce the overall confusion rate to less than 4.7% when classifying different types of attacks. We also show that our models can be used for effective classification after as few as 3 observations, suggesting the potential for application of the method in near real-time situations thereby improving safety and security.

Connected vehicles will be a prominent feature of future Intelligent Transport Systems. Which means that there will be a very high volume of wireless traffic that vehicles will receive and process. Due to this large quantity of traffic, there will be Quality of Service (QoS) constraints on the system that means messages will need to be prioritised. As vehicles will have a finite buffer to hold messages, the prioritisation scheme must consider network throughput to ensure QoS requirements are met. In our throughput authentication prioritisation technique, a Markov model is used to detect abnormally large data traffic users who are potential attackers performing a Denial of Service (DoS). Our results show that the algorithm can efficiently enhance network throughput.

Summary Experts believe that the Internet of Things (IoT) is a new revolution in technology and has brought many advantages for our society. However, there are serious challenges in terms of information security and privacy protection. Smart objects usually do not have malware detection due to resource limitations and their intrusion detection work on a particular network. Low computation power, low bandwidth, low battery, storage, and memory contribute to a resource‐constrained effect on information security and privacy protection in the domain of IoT. The capacity of fog and cloud computing such as efficient computing, data access, network and storage, supporting mobility, location awareness, heterogeneity, scalability, and low latency in secure communication positively influence information security and privacy protection in IoT. This study illustrates the positive effect of fog and cloud computing on the security of IoT systems and presents a decision‐making model based on the object's characteristics such as computational power, storage, memory, energy consumption, bandwidth, packet delivery, hop‐count, etc. This helps an IoT system choose the best nodes for creating the fog that we need in the IoT system. Our experiment shows that the proposed approach has less computational, communicational cost, and more productivity in compare with the situation that we choose the smart objects randomly to create a fog.

In this paper, we present realistic urban simulation results for store-carry-forward (SCF) relay communications within a cellular network. We describe two dynamic routing algorithms, minimising outage and minimising data packet travel time, which feature enhancements to increase the routing flexibility. It is shown that these enhancements to increase flexibility in re-routing the data leads to a dramatic decrease in the outage probability while only increasing packet travel time slightly. A misbehaviour model is analysed in this paper, in which misbehaving vehicles fail to follow the rules of the SCF routing algorithms. There are various reasons for a rogue vehicle to fail to obey the routing algorithm, including an intention to modify the message before onward transmission. Misbehaviour is detected by considering expected traffic density distributions. A Hidden Markov Model (HMM) is used to detect misbehaviour based on how data is passed by vehicles. Results show that the probability that misbehaviour is detected is 87\% using this approach.

The protection of organisational information assets requires the collaboration of all employees; information security collaboration (ISC) aggregates the efforts of employees in order to mitigate the effect of information security breaches and incidents. However, it is acknowledged that ISC formation and its development needs more investigation. This research endeavours to show how ISC forms and develops in the context of an organisation based on social bond factors. The social bond theory and theory of planned behaviour describe the effect of social bond factors on the attitude of employees and finally their behaviour regarding collaboration in the domain of information security. The results of the data analysis reveal that personal norms, involvement, and commitment to their organisation significantly influence the employees’ attitude towards ISC intention. However, contrary to the authors expectation, attachment does not influence the attitude of employees towards ISC. In addition, attitudes towards ISC, perceived behavioural control, and personal norms significantly affect the intention of employees towards ISC. The findings also show that the employees’ intention towards ISC and organisational support positively influence ISC, but that trust does not significantly affect ISC behaviour.

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. In this paper, we propose another form of deep learning, a linguistic attribute hierarchy, embedded with linguistic decision trees, for spam detection, and examine the effect of semantic attributes on the spam detection, represented by the linguistic attribute hierarchy. A case study on the SMS message database from the UCI machine learning repository has shown that a linguistic attribute hierarchy embedded with linguistic decision trees provides a transparent approach to in-depth analysing attribute impact on spam detection. This approach can not only efficiently tackle `curse of dimensionality' in spam detection with massive attributes, but also improve the performance of spam detection when the semantic attributes are constructed to a proper hierarchy.

The events of 2016 created a growing concern over the weaponization of information. Weaponized information is actually a symptom of a larger problem, namely, data fidelity. This group of researchers began considering the impact and issues that associate with data fidelity in cyber security. Presently, a fundamental universal assumption existing in cyber security solutions is that the entered data being secured is an accurate, faithful representation of the actual events that are occurring in the real world. This assumption of data fidelity is present in every major cyber security product. This work-in-progress paper acknowledges the data fidelity problem, by providing a model that couples the data object with the environment in an attempt to reduce the potential for weaponized information, thereby improving data fidelity.

The global usage of more sophisticated web-based application systems is obviously growing very rapidly. Major usage includes the storing and transporting of sensitive data over the Internet. The growth has consequently opened up a serious need for more secured network and application security protection devices. Security experts normally equip their databases with a large number of signatures to help in the detection of known web-based threats. In reality, it is almost impossible to keep updating the database with the newly identified web vulnerabilities. As such, new attacks are invisible. This research presents a novel approach of Intrusion Detection System (IDS) in detecting unknown attacks on web servers using the Unified Intrusion Anomaly Detection (UIAD) approach. The unified approach consists of three components (preprocessing, statistical analysis, and classification). Initially, the process starts with the removal of irrelevant and redundant features using a novel hybrid feature selection method. Thereafter, the process continues with the application of a statistical approach to identifying traffic abnormality. We performed Relative Percentage Ratio (RPR) coupled with Euclidean Distance Analysis (EDA) and the Chebyshev Inequality Theorem (CIT) to calculate the normality score and generate a finest threshold. Finally, Logitboost (LB) is employed alongside Random Forest (RF) as a weak classifier, with the aim of minimising the final false alarm rate. The experiment has demonstrated that our approach has successfully identified unknown attacks with greater than a 95% detection rate and less than a 1% false alarm rate for both the DARPA 1999 and the ISCX 2012 datasets.

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence.

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. Email spams can be detected through detecting senders' behaviour, the contents of an email, subject and source address, etc, while SMS spam detection usually is based on the tokens or features of messages due to short content. However, a comprehensive analysis of email/SMS content may provide cures for users to aware of email/SMS spams. We cannot completely depend on automatic tools to identify all spams. In this paper, we propose an analysis approach based on information entropy and incremental learning to see how various features affect the performance of an RBF-based SVM spam detector, so that to increase our awareness of a spam by sensing the features of a spam. The experiments were carried out on the spambase and SMSSpemCollection databases in UCI machine learning repository. The results show that some features have significant impacts on spam detection, of which users should be aware, and there exists a feature space that achieves Pareto efficiency in True Positive Rate and True Negative Rate.

In long‐term evoluation (LTE) femtocell networks, hysteresis is one of the main parameters which affects the performance of handover with a number of unnecessary handovers, including ping‐pong, early, late and incorrect handovers. In this study, the authors propose a hybrid algorithm that aims to obtain the optimised unique hysteresis for an individual mobile user moving at various speeds during the inbound handover process. This algorithm is proposed for two‐tier scenarios with macro and femto. The centralised function in this study evaluates the overall handover performance indicator. Then, the handover aggregate performance indicator (HAPI) is used to determine an optimal configuration. Based on the received reference signal‐to‐interference‐plus‐noise ratio, the distributed function residing on the user equipment (UE) is able to obtain an optimal unique hysteresis for the individual UE. Theoretical analysis with three indication boundaries is provided to evaluate the proposed algorithm. A system‐level simulation is presented, and the proposed algorithm outperformed the existing approaches in terms of handover failure, call‐drop and redundancy handover ratios and also achieved better overall system performance.

Cognitive Radio (CR) has emerged as the promising technology to address spectrum scarcity issues. One of the challenging tasks in CR networks is to agree on a common control channel to advertise the free channel list (FCL) amongst the participating CR nodes for co-operative communication. In this paper, a CR MAC protocol for searching, scanning, and accessing the control channel is proposed. The protocol consists of two levels of selection: rapid channel accessing and reliable channel accessing. In rapid channel accessing, nodes quickly and efficiently converge to a newly found control channel. In reliable channel accessing, switching to the backup control channel is performed whenever there is a PU claim on licensed channel. Furthermore, our reliable channel accessing allows CR nodes to access more than one control channel simultaneously. We evaluate the performance of the proposed approach through simulation modelling. The results show that our protocol can achieve efficient channel access time and fairness.

Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method.

Symbolic grounding in unstructured environments remains an important challenge in robotics [7]. Homecare robots are often required to be instructed by their human users intuitively, which means the robots are expected to take highlevel commands and execute corresponding tasks in a domestic environment. High-level commands are represented with symbolic terms such as “near” and “close” and, on the other hand, robots are controlled based on trajectories. The robots need to translate the symbolic terms to trajectories. In addition, domestic environment is unstructured where the same objects can be placed in different places over the time. This increases the difficulties in symbolic grounding. This paper presents a fuzzy logic based approach to symbolic grounding. In this approach, grounded concepts are modelled as fuzzy sets and the existing knowledge is used to deduce grounded values given real-time sensory inputs. Experiments results show that this approach works well in unstructured environment.

Symbolic grounding is a bridge between high-level planning and actual robot sensing, and actuation. Uncertainties raised by the unstructured environment make a bottleneck for integrating traditional artificial intelligence with service robotics. This paper presents a fuzzy logic based approach to formalise the grounding problems into a fuzzy optimization problem, which is robust to uncertainties. Novel techniques are applied to establish the objective function, to model fuzzy constraints and to perform fuzzy optimisation. The outcome is tested with a service robot fetch and carry task, where the fuzzy optimisation approach helps the robot to determine the most comfortable position (location and orientation) for grasping objects. Experimental results show that the proposed approach improves the robustness of the task implementation in unstructured environments.

Voice over IP (VoIP) is the technology used to transport real-time voice over a packet-switched network. This study analyses the effects of encrypted VoIP streams on perceived Quality of Experience (QoE) from a user's perspective. An in-depth analysis on how the transparent nature of encryption can influence the way users perceive the quality of a VoIP call have been investigated by using the E model. A series of experiments have been conducted using a representative sample of modern codecs currently employed for digitising voice, as well as three of the most commonly used iterative block ciphers for encryption (DES, 3DES, AES). It has been found that the Internet Protocol Security encryption of VoIP strongly relates to the payload sizes and choice of codecs and this relationship has different effects on the overall QoE as measured by the E model, in terms of the way that users perceive the quality of a VoIP call. The main result of this paper is that the default payload shipped with the codecs is not the optimal selection for an increased number of VoIP calls, when encryption is applied and a minimum level of QoE has to be maintained, per call.

Synchronous bandwidth, defined as the maximum time a node is allowed to send its synchronous messages while holding the token, is a sensitive parameter for deadline guarantees of synchronous messages in a timed token network. In order to offer such guarantees, synchronous bandwidth has to be allocated carefully. The allocation of synchronous bandwidths to a general synchronous message set with the minimum message deadline (Dmin) larger than the target token rotation time is studied. A new approach for allocating synchronous bandwidth, which can be easily implemented in practice, is proposed. It is demonstrated, through simulations and numerical examples, that the proposed approach performs better than any of previously proposed local synchronous bandwidth allocation schemes, in terms of its ability in guaranteeing hard real-time traffic.

The idea of converging voice and data into a best-effort service network, such as the Internet, has rapidly developed the need to effectively define the mechanisms for achieving preferential handling of traffic. This sense of QoS assurance has increased due to the enormous growth of users accessing networks, different types of traffic competing for available bandwidth and multiple services running on the core network, defined by different protocols and vendors. VoIP traffic behaviour has become a crucial element of the intrinsic QoS mainly affected by jitter, latency and packet loss rates. This paper focuses on three different mechanisms, DropTail (FIFO), RED and DiffServ, and their effects on real-time voice traffic. Measurements of jitter, end-to-end delay and packet loss, based on simulation scenarios using the NS-2 network simulator are also presented and analyzed.

In this paper we are focusing on the minimum requirements to be addressed in order to demonstrate a inter-node communication within a Virtual Organisation (VO) using the method of Self-led Critical Friends (SCF). The method is able to decide paths that a node can choose in order to locate neighbouring nodes by aiming at realizing the overhead of each communication. The weight of each path will be measured by the analysis of prerequisites in order to achieve the interaction between nodes. We define requirements as the least fundamentals that a node needs to achieve in order to determine its accessibility factor. The information gathered from an interaction is then stored in a snapshot, a profile that is made available during the discovery stage.

This paper is primarily focused on the increased IT complexity problem and the identification of security threats in networked systems. Modern networking systems, applications and services are found to be more complex in terms of integration and distribution, therefore, harder to be managed and protected. CIOs have to put their effort on threat's identification, risk management and security evaluation processes. Objective decision making requires measuring, identifying and evaluating all enterprise events, either positive (opportunities) or negative (risks) and keeping them in perspective with the business objectives. Our approach is based on a visualisation technique that helps in decision making process, focusing on the threat identification using attack scenarios. For constructing attack scenarios we use the notion of attack graphs, as well as layered security approach. The proposed onion skin model combines attack graphs and security layers to illustrate possible threats and shortest paths to the attacker's goal. By providing few examples we justify the advantage of the threat identification technique in decision making process.

The modern world increasingly requires us to prove our identity. When this has to be done remotely, as is the case when people make use of web sites, the most popular technique is the password. Unfortunately the profusion of web sites and the associated passwords reduces their efficacy and puts severe strain on users' limited cognitive resources. There is clearly a need for some creativity in terms of providing viable alternatives to passwords. This paper reports experiences of the use of a musical password, one composed of melodies instead of alphanumerics. Music is universal all over the globe and humans have superior memory for music.

Decision trees (DTs) provide an attractive classification scheme because clinicians responsible for making reliable decisions can easily interpret them. Bayesian averaging over DTs allows clinicians to evaluate the class posterior distribution and therefore to estimate the risk of making misleading decisions. The use of Markov chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. The Reversible Jump (RJ) extension of MCMC allows sampling from DTs of different sizes. However, the RJ MCMC process may become stuck in a particular DT far away from the region with maximal posterior. This negative effect can be mitigated by averaging the DTs obtained in different starts. In this paper we describe a new approach based on an adaptive sampling scheme. The performances of Bayesian DT techniques with the restarting and adaptive strategies are compared on a synthetic dataset as well as on some medical datasets. By quantitatively evaluating the classification uncertainty, we found that the adaptive strategy is superior to the restarting strategy.

Radio networks of universal mobile telecommunication system (UMTS) need accurate planning and optimisation, and many factors not seen in second generation (2G) networks must be considered. However, planning and optimisation of UMTS radio networks are often carried out with static simulations, for efficiency and to save time. To obtain a good trade-off between accuracy and computational load, link-level performance factors need to be taken into account. The authors propose a mathematical model for UMTS radio network planning taking into consideration fast power control, soft handover and pilot signal power in both uplink and downlink. Optimisation strategies are investigated based on three meta-heuristics: genetic algorithm, simulated annealing (SA) and evolutionary-SA. The base station location problem is modelled as a simplified p-median problem, and parameter tuning of these meta-heuristics are presented. Extensive experimental results are used to compare the performance of different algorithms in terms of statistical measurements.

The increasing growth in the application of global computing and pervasive systems has necessitated careful consideration of security issues. In particular, there has been a growth in the use of electronic communities, in which there exist many relationships between different entities. Such relationships require establishing trust between entities and a great deal of effort has been expended in developing accurate and reliable models of trust in such multi-client environments. Many of these models are complex and not necessarily guaranteed to give accurate trust predictions. In this paper we present a review of some of these models before proposing a simple, lightweight model for trust. The proposed model does not require the estimation of a large parameter set, nor make great assumptions about the parameters that affect trust.

The introduction and evolution of security standards for wireless networking has been a problematic process. Flaws in the initial security standard resulted in quick-fix solutions and interoperability issues. As wireless networks are not confined to a building, there is an added security risk that radio signals can be detected externally. Wireless networking has rapidly increased in popularity over the last few years due to the flexibility it provides. Given the simultaneous growth of e-government services there is particular risk to the citizen of identity theft. This article discusses the progression of wireless security protocols since their introduction and the effect this has had on home and business users. The risks of using wireless networks are outlined in the paper and recommendations for securing wireless networks are reviewed.

This paper discusses usability and practicality issues for authentication systems based on biometrics. The effectiveness of a system incorporating an authentication method depends not only on theoretical and technological issues, but also on user interaction with and practical implementation of the system by an organisation. It is becoming increasingly common that IT and physical security are converging, especially in the workplace. This has significant ramifications for the workforce and operational matters. In this paper we pay particular attention to the potential issues that arise when companies introduce biometrics for IT or physical security and provide recommendations that help ensure a usable and practical implementation of the technology.

The issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems.

Traditional approaches to trust, be it in agent-based societies, or within a more theoretical framework often consider trust to be a local phenomenon. Here we propose that trust should be viewed from a global perspective. Our motivation is the area of pervasive computing although we believe that our formal framework applies in many domains. Here we present our framework and formalize it in the form of graph theory. We present some open problems and discuss the wider application of our work

Binary tree is a graph, without cycle, that is frequently used in computer science for fast data access and retrieval. To ensure faster insertion and deletion, the tree height has to be kept to a minimum. A random tree starts loosing its randomness after a series of insertions and deletions and, in the worst case, a tree with n nodes, could grow up to the height of n-1. In this paper, we present modified insertion and deletion algorithms to maintain the tree in better shape dynamically. Without applying any complex rebalancing technique, or using considerable amount of space, both algorithms maintain the tree in such a way that even a series of insertions and asymmetric deletions do not cause the tree to grow beyond n/2. A comparative study of traditional and modified insert algorithms shows that for random input, the modified insert algorithm produces a tree with 20% to 30% reduction in height, forcing the average number of comparisons required for a successful search to go down by 15% to 20%.

There is increasing interest in the use of multimedia and Web based materials for teaching and learning; areas of particular significance for the use of enhanced learning materials are those that may be inaccessible to certain student populations. Environments such as building sites present unique safety and access difficulties for visits and are particularly suitable for virtual exploration using interactive software. In this paper, we present a tool that provides multiple cross linked digital media of a construction site, address some of the issues in the choice of delivery format and present an initial assessment of the efficacy of the software for teaching and learning.

The area of path planning has received a great deal of attention recently. Algorithms are required that can deliver optimal paths for robots to take over homogeneous or non-homogeneous terrain. Optimal paths may be those that involve the shortest distance travelled, the least number of turns or the least number of ascents and descents. The often highly complex nature of terrains and the necessity for realtime solutions have lead to a requirement for the development of parallel algorithms. Such problems have been notoriously difficult to parallelise efficiently; indeed it has been said that an efficiency of 25-60% should be considered a success. In this paper we present a parallel algorithm for finding optimal paths over non-homogeneous terrain that demonstrates superlinear speed-up.

Graph representations are widely used for dealing with structural information. There are applications, for example, in pattern recognition, machine learning and information retrieval, where one needs to measure the similarity of objects. When graphs are used for the representation of structured objects, then measuring the similarity of objects becomes equivalent to determining the similarity of graphs. The measurement of similarity is normally performed by determining the maximum common subgraph of the graphs in question. This paper presents a new algorithm for determining the maximum common subgraph of a pair of graphs which offers better performance than existing algorithms.

There is increasing use of coordinated and multiple views for the study of complex data. We discuss the use of multiple coordinated views as an aid to navigation and orientation through three-dimensional virtual environments. Such virtual environments have many applications including military, medical and educational uses. We determine whether there is an ease of navigation and orientation arising from offering users multiple views.

In this paper we present method for finding similarities in a pair of three-dimensional objects. The method involves obtaining boundary cubes approximations to the two objects, see (Maple and Donafee, 2002). The boundary cubes algorithm is a modification to the well-known marching cubes algorithm of Lorensen and Cline (1987). Having obtained the approximations we can apply exact and inexact graph-matching algorithms to quantify the similarity between two objects. This paper considers methods for exact and inexact graph matching and provides novel and efficient algorithms for graph matching applied to boundary cubes representations.

A spherical picture is a useful way to visualise a group presentation. Spherical pictures are essentially planar, possibly nonsimple, graphs. We present a graph drawing algorithm that produces spherical pictures. The algorithm has been encompassed into a software package, SPICE, and output is presented.

Many algorithms exist to determine if a given graph can be embedded in a plane [G. Di Battistia et al., (1994)]. The majority of these methods, however, are only valid for simple graphs and do not take into account the order of edges emanating from each vertex. There are many areas, such as communication design, genetics, group theory, network optimisation and VLSI, where the ordering of edges is crucial to the representation of a system. Rotation schemes can be used to store the ordering of edges around a vertex. Let G be an arbitrary, possibly nonsimple, graph. We provide an algorithm that determines, from the rotation scheme of G, if G can be embedded in the plane. If the rotation scheme of G can be realised by a planar drawing, the regions of G are returned.

We present a method for representing important conserved features of a protein directly involved in binding. The conserved features are observed by noticing common atoms in proteins that bind a ligand. A method for constructing a fingerprint based upon the geometric configuration of the atoms in the binding region of the protein is presented. The fingerprint can be applied to other protein structural PDB (protein data bank) files to facilitate identification of the specific binding site the fingerprint represents. The algorithm presented complements software developed at the University of Luton, TMSite, and can be used with the ClustalW alignment tool. By matching one amino acid of the fingerprint onto a new protein, then using it as an anchor and searching for the other corresponding amino acids around it gives a simple method to find possible binding sites. The more points of the fingerprint matched the less likely the match is to be coincidental. Thresholds, both geometrical and biochemical, can be altered to adjust sensitivity of the search. This method will be implemented in TMBoundary, a current software project, as an alternative or complementary search method for binding sites in proteins of known structure.

We present a data retrieval and information visualisation tool, named the object-oriented graph editor (OOGE). The OOGE is designed to represent information of special kinds of system, hierarchical systems or object-oriented systems. These systems can be represented by object-oriented graph, in which nodes have attributes and their connections are constraint-based. The editor presented provides a friendly graphical user interface for users to define any class of nodes of the OO systems and to draw the OO graph based upon the user-defined node classes. The validity of the proposed system can be checked against the set of constraints that are based on the attributes of the user-defined classes.

We present a "lightweight" visual formalism that can be used to examine the state space complexity of an interface. The method can form a basis for designing, testing and documenting the 'front-end' component of a typical GUI forms based interface. The standard is intended to be free standing and seeks to address issues specific to forms based GUI interfaces. The method is based on a state transition diagram (STD) notation that is used to model the GUI component. STDs can be used at an early stage in the design of an interface to quantify and control surface level complexity. Further to allowing a visualisation of the complexity of an interface, techniques used in graph theory are applied to determine a novel measure of complexity that can then be used to better usability.

A novel method is provided for the representation and comparison of boundaries of two-dimensional objects. The work builds upon the rotating squares algorithm of Donafee and Maple (2000), a method based upon the marching cubes algorithm of Lorensen and Cline (1987). The rotating squares method for representing and comparing the boundaries of two-dimensional objects differs from standard polygonal approximations of objects in the sense that, though more space is required for data storage, there is no loss of data as is the case with approximations. This work presents a new method for polygonal approximation and details of a hybrid method whereby polygonal approximations are used for initial boundary comparisons and then the exact data is used for further investigation.

We present a method for identifying and representing indentations, cavities and holes in a known protein structure. The identification procedure is performed initially. Once a pocket in the protein has been found, we use a boundary representation technique to represent it. Both the identification and the representation procedures rely upon modifications to the Marching Cubes algorithm of Lorensen and Cline (1987), which can be used to give a piecewise planar representation of a three-dimensional object. The identification of a pocket essentially involves finding a point, (x/sub 0/, y/sub 0/, z/sub 0/) say. The point should be such that a sphere of radius r can he centred there, without touching the centre of any atom in the protein, but there exists R>r such that a sphere of radius R centred at (x/sub 0/, y/sub 0/, z/sub 0/) does touch the centre of an atom. If such a point is found, then it is deemed to be in a pocket. The boundary representation technique involves storing information about a boundary in the structure of a graph. The graph structure of a pocket, potential docking site, can then be compared to the graph structure of a candidate binding substrate using standard graph comparison algorithms. The required input for the algorithm is in the form of a Protein Data Bank (PDB) file.

The marching cubes algorithm of W.E. Lorensen and H.E. Cline (1987) is a method that is used frequently in 3D object visualisation. The underlying concept of the technique is to form piecewise planar approximations to surfaces, for which pre-determined normals are used for shading purposes. It should be noted that these approximations are precisely as accurate as the data given; there is no loss in quality or error introduced. The approach of using these cubes to represent data can also be used to fully describe the piecewise planar approximation to the surface. Problems arise however, in terms of the storage space required for the representation of large objects, and it is this issue that is addressed in this paper. We present an algorithm that transforms pixel-wise data into a boundary representation that has potential for large reductions in storage. The algorithm does not involve any further approximation of the surface of the object than that given as input. The algorithm also provides a structure that makes surface analysis and matching very simple.

Rotating Squares is a method based upon the Marching Cubes algorithm of Lorensen and Cline (1987). It is used as a method for storing a piecewise linear approximation of a two dimensional object (Donafee, 2000). Approximations to two distinct shapes can then be easily compared to find common boundary segments. However, it is also possible to use the algorithm to determine how to align the two shapes in a plane, such that they have the longest possible boundary segment in common. It is this issue that is addressed in the paper. Examples are given in which, while finding alike boundary segments, the Rotating Squares algorithm does not provide an edge along which the two shapes can be aligned without intruding upon one another. We present a simple test that can be used to indicate any space-sharing violation. Since the motivation for this work arose in three dimensions we go on to give considerations necessary for lifting the algorithms to three dimensions.