In the commonly used method of bolting to secure parts of equipment and structure, the bolts must be tightened to an adequate preload force. Failure to do so could affect the integrity of the structure, as well as the efficient running of the site and, crucially, employees’ safety. In this project, we consider the use of artificial intelligence (AI) techniques to analyse maintenance videos and identify the unwanted loosening of bolts over time in order that they might be used as additional tools in a continuous maintenance plan. We found that accuracy levels of up to 97% could be achieved in identifying bolt rotation with our proposed machine learning-based triplet loss architecture. The use of gradient-weighted class activation mapping (Grad-CAM) visualisations to identify areas of the image where change had occurred enabled us to test how robust our model was to noise in the data. This explanation may assist users in safety-critical environments guiding them to the problem, and helping mitigate the black-box nature of machine learning algorithms. Whilst the accuracy of the models varies depending on the rotational angle of the bolt, we clearly demonstrate that triplet loss is a good basis for performing change detection in industrial settings. Furthermore, Grad-CAM has shown to be a useful technique to help a user understand the decisions made by the network and allow them to see where unwanted rotation has occurred.

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic data, improving IDS robustness and accuracy against both traditional and adversarial attacks.

Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, anovel5G automobile network emergency situationsbased onChebyshev polynomial using fog computing and authentication is proposed.This is the first study to useChebyshev chaotic mapping algorithmthatuses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme hasfivestages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. Theformalsecurity analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response.The evaluation showed thatthe proposed method cannot execute forgery, impersonation, or replay attacks.Theproposed method has lower computational, communication, and storage overhead than earlier efforts.

In the security domain, the growing need for reliable authentication methods highlights the importance of thermal face recognition for enhancing law enforcement surveillance and safety especially in IoT applications. Challenges like computational resources and alterations in facial appearance, e.g., plastic surgery could affect face recognition systems. This study presents a novel, robust thermal face recognition model tailored for law enforcement, leveraging thermal signatures from facial blood vessels using a new CNN architecture (Max and Average Pooling- MAP-CNN). This architecture addresses expression, illumination, and surgical invariance, providing a robust feature set critical for precise recognition in law enforcement and border control. Additionally, the model employs the NM-PSO algorithm, integrating neighborhood multi-granulation rough set (NMGRS) with particle swarm optimization (PSO), which efficiently handles both categorical and numerical data from multi-granulation perspectives, leading to a 57% reduction in feature dimensions while maintaining high classification accuracy outperforming ten contemporary models on the Charlotte-ThermalFace dataset by about 10% across key metrics. Rigorous statistical tests confirm NM-PSO’s superiority, and further robustness testing of the face recognition model against image ambiguity and missing data demonstrated its consistent performance, enhancing its suitability for security-sensitive environments with 99% classification accuracy.

Phishing involves malicious activity whereby phishers, in the disguise of legitimate entities, obtain illegitimate access to the victims’ personal and private information, usually through emails. Currently, phishing attacks and threats are being handled effectively through the use of the latest phishing email detection solutions. Most current phishing detection systems assume phishing attacks to be in English, though attacks in other languages are growing. In particular, Arabic is a widely used language and therefore represents a vulnerable target. However, there is a significant shortage of corpora that can be used to develop Arabic phishing detection systems. This article presents the development of a new English-Arabic parallel phishing email corpus that has been developed from the anti-phishing share task text (IWSPA-AP 2018). The email content was to be translated, and the task had been allotted to 10 volunteers who had a university background and were English and Arabic language experts. To evaluate the effectiveness of the new corpus, we develop phishing email detection models using Term Frequency–Inverse Document Frequency and Multilayer Perceptron using 1,258 emails in Arabic and English that have equal ratios of legitimate and phishing emails. The experimental findings show that the accuracy reaches 96.82% for the Arabic dataset and 94.63% for the emails in English, providing some assurance of the potential value of the parallel corpus developed.

Agile methods are a well-established paradigm in the software development field. Agile adoption has contributed to improving software quality. However, software products are vulnerable to security challenges and susceptible to cyberattacks. This study aims to improve security of software products when using an agile software development process. A multi-methods qualitative research approach was adopted in this study. First, we conducted semi-structured interviews with 23 agile practitioners having varied years of cybersecurity experiences. An approach informed by grounded theory methodology was adopted for data analysis. Second, we developed a novel practice-based agile software development process model derived from the results of the data analysis conducted. Third, we validated the model through a focus group comprising five senior agile cybersecurity professionals to evaluate its relevancy and novelty. The study has identified 26 security practices, organized into the six - software development life-cycle phases: planning, requirements, design, implementation, testing, and deployment. We have mapped the practices onto four swim lanes each representing an agile role. The self-organizing team is exclusively involved in three security practices, the security specialist in nine, penetration tester in one and the DevOps team collaborates on one with the security specialist. There are also seven practices that are collaboratively performed by the self-organizing team and the security specialist. Each of the practices in the model was examined during the validation phase of the study. There are two contributions in this study. First, the paper proposes a novel practice-based model comprising of 26 security practices mapped to agile roles. Second, we propose a new practice, in response to an observed lack of collaborative ceremonies, to disseminate awareness of and hence compliance with security standards.

Expanded complementary writing reflecting on the multi-output PAR project, including insights assembled and gleaned for a presentation for Storytellers and Machines (2024 conference at Manchester Metropolitan University) and the overall, 3-stage process of BOBBY....'s development (2019, 2021 and 2024).

Digital Right Management (DRM) is a technology developed to prevent illegal reproduction and distribution of digital contents. It protects the rights of content owners by allowing only authorised consumers to legitimately access associated digital content. DRM systems typically use a consumer's identity for authentication. In addition, some DRM systems collect consumer's preferences to obtain a content license. Thus, the behaviour of DRM systems disadvantages the digital content consumers (i.e. neglecting consumers' privacy) focusing more on securing the digital content (i.e. biased towards content owners). This paper proposes the Privacy-Preserving Digital Rights Management System (PrivDRM) that allows a consumer to acquire digital content with its license without disclosing complete personal information and without using any third parties. To evaluate the performance of the proposed solution, a prototype of the PrivDRM system has been developed and investigated. The security analysis (attacks and threats) are analysed and showed that PrivDRM supports countermeasures for well-known attacks and achieving the privacy requirements. In addition, a comparison with some well-known proposals shows that PrivDRM outperforms those proposals in terms of processing overhead.

Breast cancer is one from various diseases that has got great attention in the last decades.This due to the number of women who died because of this disease.Segmentation is always an important step in developing a CAD system.This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms.This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database.To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed.Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features.The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%).Also, using the measurements of the recall and the precision, the classification results reached 100%.This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

In this paper, a novel approach, called Repeated Reselling Permission based Multiple Reselling (RRP-MR), is proposed. This approach allows a consumer who has bought his license from another consumer to resell this license to a third consumer. This reselling process continues till the license is resold N-times. This multiple reselling process is achieved without compromising neither the content owner's rights nor the consumer's rights. In addition, the RRP-MR approach not only provide secure license reselling process but also fair license reselling between the two involved consumers. Also, the RRP-MR gives the buyer the power to stop the multiple resellings of the license at a given reselling process. Furthermore, it enables the buyer to detect any fake license reselling at the first step of the reselling process. The RRP-MR approach makes use of a License Issuer (LI), License Revocation List (LRL), digital token called Reselling Permission (RP) and a special contract signing protocol known as Reselling Deal Signing (RDS) protocol to achieve fair and secure reselling. An analysis of potential threats has shown that this approach has met its security objectives.