The banking and financial sector has always been a prime target for cyber threats due to the critical nature of the information they handle. With the increasing dependence on technology and digital transformation, the sector is facing more complex and sophisticated threats from cybercriminals. The banking sector serves as the backbone of a country’s economy, interlinked with various other sectors like petroleum, mining, health, and industry. Any significant damage to the banking sector can send shockwaves through the entire economic landscape. Therefore, cyber threat classifications play a crucial role in risk management and provide a valuable framework for understanding and responding to cyber threats. Understanding the potential impact of a cyber-attack helps organisations assess the risks they face and develop appropriate risk mitigation strategies. The purpose of this research paper is to provide a comprehensive analysis of cyber threats in the banking and financial sectors, including identifying common threats, their nature and character to help in classification. One of the significant contributions of this research paper is to classify cyber threats to the banking and financial sectors based on their severity and technicality. This classification helps to identify the appropriate countermeasures required to mitigate the risks of each type of threat. Furthermore, the paper explores the technical, non-technical, organizational countermeasures and the legal and regulatory measures used to protect financial transactions from cyber threats. This research work delves into the challenges and limitations of cyber threat classifications, focusing specifically on those confronting the banking and financial sector in their pursuit of robust cybersecurity. Additionally, it analyses recent trends and developments in the field, highlighting the evolving nature of cyber threats to banking. The most significant challenge is the rapidly evolving nature of cyber threats, making it challenging to keep up with the latest trends and technologies.

Background/Objectives: Artificial intelligence-based software as a medical device (AI-SaMD) refers to AI-powered software used for medical purposes without being embedded in physical devices. Despite increasing approvals over the past decade, research in this domain—spanning technology, healthcare, and national security—remains limited. This research aims to bridge the existing research gap in AI-SaMD by systematically reviewing the literature from the past decade, with the aim of classifying key findings, identifying critical challenges, and synthesizing insights related to technological, clinical, and regulatory aspects of AI-SaMD. Methods: A systematic literature review based on the PRISMA framework was performed to select the relevant AI-SaMD studies published between 2015 and 2024 in order to uncover key themes such as publication venues, geographical trends, key challenges, and research gaps. Results: Most studies focus on specialized clinical settings like radiology and ophthalmology rather than general clinical practice. Key challenges to implement AI-SaMD include regulatory issues (e.g., regulatory frameworks), AI malpractice (e.g., explainability and expert oversight), and data governance (e.g., privacy and data sharing). Existing research emphasizes the importance of (1) addressing the regulatory problems through the specific duties of regulatory authorities, (2) interdisciplinary collaboration, (3) clinician training, (4) the seamless integration of AI-SaMD with healthcare software systems (e.g., electronic health records), and (5) the rigorous validation of AI-SaMD models to ensure effective implementation. Conclusions: This study offers valuable insights for diverse stakeholders, emphasizing the need to move beyond theoretical analyses and prioritize practical, experimental research to advance the real-world application of AI-SaMDs. This study concludes by outlining future research directions and emphasizing the limitations of the predominantly theoretical approaches currently available.

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg’s effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.

Software obfuscation techniques are increasingly being used to prevent attackers from exploiting security flaws and launching successful attacks. With research on software obfuscation techniques rapidly growing, many software obfuscation techniques with varying quality and strength have been proposed in the literature. However, the literature on obfuscation techniques has not yet been coherently collated and reviewed. This research paper aims to present an overview of state-of-the-art software obfuscation techniques, focusing on quality and strength. A systematic analysis and synthesis of literature published between 2010 and April 2021 has been performed to identify the common measures to quantify obfuscation and their measures, the publication venue, and the home country of the researchers. We have identified the obfuscation quality attributes, such as potency, resilience, cost, stealth, and similarity, that are the most widely used metrics to evaluate the quality of obfuscation techniques. In addition, different measures have been used to quantify these qualities, such as complexity (to measure potency), human effort (to measure resilience), efficiency (to estimate cost), and multiclass performance metrics, distance measures, and matching method (to quantify similarity). These measures were then categorized into sub-measures. The literature lacks research in the following two areas: empirical research using a case study strategy, i.e., real-world datasets, and measurements of obfuscation stealth. Researchers did not address stealth as clearly as they addressed potency, cost, and similarity.

Information and communication technology (ICT) projects still suffer from high failure rates, especially in emerging markets. This paper investigates existing trends and influencing factors for this failure in such a context, using a representative sample of organizations in Saudi Arabia (SA). To this end, two sources of data collection are used, a corporate survey on local and multinational organizations working in SA including a qualitative analysis of the participants’ answers, and archival data of a real-world case study of a challenging software project. The management/organization factors including lack of planning, resistance to change, misunderstanding user requirements, government regulations, poor business process reengineering, and lack of training were found to be the key indicators that influence ICT project failures. However, technical factors (e.g. redesign required for government compliance such as the use of the Hijri calendar) and financial reasons tend to have reduced influence because of extensive government subsidization in such countries. To generalize our findings, we compare and discuss our results with another Middle Eastern market. The comparison found that the lack of planning is the most important factor of failure between the two countries. The results may also apply to other emerging markets.