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.

This study presents a novel Similarity-Based Hybrid API Malware Detection Model (HAPI-MDM) aiming to enhance the accuracy of malware detection by leveraging the combined strengths of static and dynamic analysis of API calls. Faced with the pervasive challenge of obfuscation techniques used by malware authors, the conventional detection models often struggle to maintain robust performance. Our proposed model addresses this issue by deploying a two-stage learning approach where the XGBoost algorithm acts as a feature extractor feeding into an Artificial Neural Network (ANN). The key innovation of HAPI-MDM is the similarity-based feature, which further enhances the detection accuracy of the dynamic analysis, ensuring reliable detection even in the presence of obfuscation. The model was evaluated using seven machine learning techniques with 10 K-fold cross-validation. Experimental results demonstrated HAPI-MDM’s superior performance, achieving an overall accuracy of 97.91% and the lowest false-positive and false-negative rates compared to related works. The findings suggest that integrating dynamic and static API-based features and utilizing a similarity-based feature significantly improves malware detection performance, thereby offering an effective tool to fortify cybersecurity measures against escalating malware threats.

Hazardous open-pit mining environments require delay-sensitive computation offloading mechanisms that protect geographically disadvantaged Industrial Internet of Things (IIoT) devices rather than optimizing only system-wide averages. This paper proposes Fairness-Oriented Resource Optimization (FAIROPT), a fairness-oriented optimization framework for Intelligent Reflecting Surface (IRS)-aided Unmanned Aerial Vehicle (UAV)-Mobile Edge Computing (MEC) networks operating under database-assisted spectrum sharing. Motivated by Ultra Reliable and Low Latency Communications (URLLC)-type requirements, the proposed framework focuses on worst-case task-completion latency reduction and fairness enhancement, rather than claiming a complete URLLC guarantee in terms of finite-blocklength reliability, packet error probability, or probabilistic outage constraints. We formulate an Non deterministic Polynomial time (NP)-hard Mixed Integer Non Linear Programming (MINLP) problem that jointly optimizes partial computation offloading, edge Central Processing Unit (CPU) allocation, subcarrier assignment, transmit power, hardware-impaired IRS phase shifts, and static 3D UAV placement under max-min latency, interference-temperature, and energy constraints. To solve this problem, we develop a Block Coordinate Descent (BCD)-inspired iterative framework featuring: (i) closed-form or projected analytical updates for offloading, edge CPU, subcarrier, and power allocation; (ii) hardware-aware IRS phase optimization with quadratic refinement that captures amplitude-phase coupling; and (iii) derivative-free Enhanced Pattern Search (EPS) for static UAV placement under the Hover-and-Compute protocol. Because the IRS and UAV updates rely on surrogate and heuristic steps, respectively, we establish monotonicity of the accepted worst-case latency sequence with safeguard checks and verify empirical convergence through simulations. Simulation results in a 500 m \(\times \) 500 m mining scenario show that FAIR-OPT stabilizes near 1.48 s worst-case latency, reducing latency by approximately 43.3% compared with No-IRS and by approximately