Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.

Perceiving and understanding cyber-attacks can be a difficult task. This problem is widely recognised and well documented, and more effective techniques are needed to aid cyber-attack perception. Attack modelling techniques (AMTs) –such as attack graphs and fault trees, are useful visual aids that can aid cyber-attack perception; however, there is little empirical or comparative research which evaluates the effectiveness of these methods. This paper reports the results of an empirical evaluation between an adapted attack graph method and the fault tree standard to determine which of the two methods is more effective in aiding cyber-attack perception. An empirical evaluation (n=63) was conducted through a 3×2×2 factorial design. Participants from computer-science and non computer-science backgrounds were divided into an adapted attack graph and fault tree group and then asked to complete three tests which tested the ability to recall, comprehend and apply the attack modelling technique. A mean assessment score (mas) was calculated for each test. The results show that the adapted attack graph method is more effective at aiding cyber-attack perception when compared with the fault tree method (p <0.01). Participants that have a computer science background outperformed other participants when using both methods (p <0.05). These results indicate that the adapted attack graph method can be an effective tool for aiding cyber-attack perception amongst experts. The study underlines the need for further comparisons in a broader range of settings involving additional techniques, and suggests several suggestions for further work.

Universities hold and process vast amounts of financial, user, and research data, which makes them prime targets for cybercriminals. In addition to the usual external threat actors, universities face a unique insider threat from students, who—alongside staff—may lack adequate cyber security training despite having access to various sensitive systems. This paper provides a focused assessment of the current cyber security threats facing UK universities, based on a comprehensive review of available information. A chronological timeline of notable cyber attacks against universities is produced, with incidents classified according to the CIA triad (Confidentiality, Integrity, Availability) and incident type. Several issues have been identified. Limited disclosure of attack details is a major concern, as full information is often withheld for security reasons, hindering institutions’ abilities to assess vulnerabilities thoroughly and respond effectively. Additionally, universities increasingly rely on third-party service providers for critical services, meaning that an attack on these external providers can directly impact university operations and data security. While SQL injection attacks, previously a significant issue, appear to have declined in frequency—perhaps reflecting improvements in defences—other threats continue to persist. Universities report lower levels of concern regarding DDoS attacks, potentially due to enhanced resilience and mitigation strategies; however, ransomware and phishing attacks remain prevalent. Insider threats, especially from students with varied IT skills, exacerbate these risks, as insiders may unknowingly or maliciously facilitate cyber attacks, posing ongoing challenges for university IT teams. This study recommends that universities leverage these insights, along with other available data, to refine their cyber security strategies. Developing targeted policies, strengthening training, and implementing international standards will allow universities to enhance their security posture and mitigate the complex and evolving threats they face.

Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.

This paper expands the extended unified theory of acceptance and use of technology (UTAUT2) to include four additional constructs (security, risk, institutional trust, and technology trust), providing a more comprehensive understanding of mobile banking applications (m-banking apps) adoption. It also highlights the significant role of demographic factors in moderating the impact of these constructs, offering practical insights for promoting the use of mobile devices to access and manage banking services. Data were collected using an online survey from 315 mobile banking users and analysed using covariance-based structural equation modelling (CB-SEM). Most constructs of the baseline UTAUT2 were validated in the m-banking context, with the additional constructs confirmed to affect user intention to adopt m-banking apps, except perceived risk. The model explained 79% of the variance in behavioural intention (BI), and 54.7% in use behaviour (UB), achieving higher fit than the baseline UTAUT2. Age, gender, experience, income, and education moderated the impact of perceived security and institutional trust on BI; age, education, and experience moderated technology trust on BI; and age, gender, and experience moderated perceived security on UB. The guarantee of enhanced security, advanced privacy mechanisms, and trust should be considered paramount in future strategies aimed at promoting m-banking app adoption and use. Overall, the paper advances scientific knowledge by providing a more nuanced and comprehensive framework for understanding m-banking app adoption, validating new constructs, and offering practical recommendations for promoting m-banking usage.