2025 September - 42 articles
The rapid expansion of mobile financial services (MFSs) has brought about benefits in terms of financial inclusion in developing countries; however, threats have also emerged on the sides of cybersecurity and privacy. Traditional fraud-detection stra...
The growing reliance on satellite-based infrastructures for communication, navigation, defense, and environmental monitoring has magnified the urgency of securing Space Information Networks (SINs) against cyber threats. This paper presents a comprehe...
Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to...
Ensuring data privacy and security in sensitive domains such as healthcare remains a critical challenge. Homomorphic Encryption (HE) offers a promising approach by enabling computations directly on encrypted data, but the diversity of available schem...
Supervisory Control and Data Acquisition (SCADA) systems play a critical role in industrial processes by providing real-time monitoring and control of equipment across large-scale, distributed operations. In the context of cyber security, Intrusion D...
Accurate malware family classification from dynamic sandbox reports continues to be a fundamental cybersecurity challenge. Most prior works depend on random splits that tend to overestimate accuracy, whereas deployment requires robustness under tempo...
Large language models (LLMs) have become essential in various use cases, such as code generation, reasoning, or translation. Applications vary from language understanding to decision making. Despite this rapid evolution, significant concerns appear r...
Ransomware is a fast-evolving form of cybercrime in which a ransom is demanded to restore access to a victim’s encrypted files. The business model of the criminals relies on victims being willing to pay the ransom demand. In this paper we use i...
Electronic health record (EHR) data breaches create severe concerns for patients’ privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive dat...
Modern network intrusion detection systems (NIDSs) rely on complex deep learning models. However, the “black-box” nature of deep learning methods hinders transparency and trust in predictions, preventing the timely implementation of count...
This study presents a novel and interpretable, deployment-ready framework for predicting cybersecurity incidents through item-level behavioral, cognitive, and dispositional indicators. Based on survey data from 453 professionals across countries and...
Obligations in the Next-Generation Access Control (NGAC) standard enable the development of security-intensive workflow systems where access privileges evolve over time. However, specifying obligations for dynamic access requirements poses challenges...
In this literature review, we critically examine the evolving landscape of privacy in blockchain systems, with a particular focus on the differentiation of privacy attacks and protective measures across three distinct layers: the on-chain layer; the...
Although many chaos-based cryptosystems have been proposed over the past decade, they have yet to gain traction in real-world applications. A key reason for this is that most designs rely on security through obscurity, with unnecessarily complex stru...
The increasing complexity and scale of cyber threats demand advanced, automated methodologies for extracting actionable cyber threat intelligence (CTI). The automated extraction of Tactics, Techniques, and Procedures (TTPs) from unstructured threat r...
Traditional encryption prioritises confidentiality but complicates search operations, requiring decryption before searches can be conducted. The public key encryption with keyword search (PEKS) scheme addresses this limitation by enabling authorised...
Ransomware attacks pose a serious threat to global cybersecurity, inflicting severe financial and operational damage on organizations, individuals, and critical infrastructure. Despite their pervasive impact, proactive measures to mitigate ransomware...
The growing sophistication of cyber threats has posed significant challenges for organizations in terms of accurately detecting and responding to incidents in a coordinated manner. Despite advances in the application of machine learning and automatio...
This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack...
The proper use of Android app permissions is crucial to the success and security of these apps. Users must agree to permission requests when installing or running their apps. Despite official Android platform documentation on proper permission usage,...
Differential privacy (DP) is an important framework to provide strong theoretical guarantees on the privacy and utility of released data. Since its introduction in 2006, DP has been applied to various data types and domains. More recently, the introd...
In today’s era of technology, where information is readily available anytime and from anywhere, safeguarding our privacy and sensitive data is more important than ever. The thermal sensors embedded within a CPU are primarily utilized for monito...
The increasing complexity and volume of cybersecurity logs demand advanced analytical techniques capable of accurate threat detection and explainability. This paper investigates the application of Large Language Models (LLMs), specifically qwen2.5:7b...
Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalys...
In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number o...
As Android malware grows increasingly sophisticated, traditional detection methods struggle to keep pace, creating an urgent need for robust, interpretable, and real-time solutions to safeguard mobile ecosystems. This study introduces YoloMal-XAI, a...
This study explores the emergent dynamics of knowledge sovereignty within organisations following data breach incidents. Using qualitative analysis based on Benoit’s image restoration theory, this study shows that employees do more than relay o...
Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are tru...
End-users in a decision-oriented Internet of Things (IoT) healthcare system are often left in the dark regarding critical security information necessary for making informed decisions about potential risks. This is partly due to the lack of transparen...
The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent...
With the increasing sophistication of network attacks, machine learning (ML)-based methods have showcased promising performance in attack detection. However, ML-based methods often suffer from high false rates when tackling encrypted malicious traffi...
Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious...
The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning us...
Record linkage can enhance the utility of data by bringing data together from different sources, increasing the available information about data subjects and providing more holistic views. Doing so, however, can increase privacy risks. To mitigate th...
The widespread rise of misinformation across digital platforms has increased the demand for accurate and efficient Fake News Detection (FND) systems. This study introduces an enhanced transformer-based architecture for FND, developed through comprehe...
The emergence of quantum computing and Shor’s algorithm necessitates an imminent shift from current public key cryptography techniques to post-quantum-robust techniques. The NIST has responded by standardising Post-Quantum Cryptography (PQC) al...
Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated lear...
The European Union’s Artificial Intelligence Act (EU AI Act) is expected to be a major legal breakthrough in an attempt to tame AI’s negative aspects by setting common rules and obligations for companies active in the EU Single Market. Gl...
Hyperledger Fabric (HLF) is a leading permissioned blockchain platform designed for enterprise applications. However, it faces significant security risks from Denial-of-Service (DoS) attacks targeting its core components. This study systematically in...
This study investigates the effectiveness of two cybersecurity awareness interventions—phishing simulations and organized online training—in enhancing end-user resilience to phishing attacks in a Croatian university setting. Three control...
Federated learning enables privacy-preserving model training across distributed clients, yet real-world deployments face statistical, system, and behavioral heterogeneity, which degrades performance and increases vulnerability to adversarial clients....
While Local Differential Privacy (LDP) offers strong privacy guarantees for IoT data collection, users often struggle to understand its implications and control their privacy settings. This paper presents a user-centric approach to implementing LDP i...
