Darlington Chigozie Okeke^*

Journal of Cyber Security, Vol.8, pp. 281-317, 2026, DOI:10.32604/jcs.2026.079750 - 29 May 2026

Abstract Phishing has become the most common cybersecurity threat and increasingly exploits human factors rather than technical vulnerabilities. This study examined the relationships between cybersecurity awareness, training frequency, user cyber-hygiene behaviour, organisational culture, risk perception, and self-reported phishing vulnerability and the theoretical basis of this research is the Technology Threat Avoidance Theory (TTAT). A quantitative correlational design was used for data collection and analysis with Pearson correlation in structured questionnaires. The results indicated that the five independent variables have a significant positive relationship with phishing vulnerability. The increased awareness and regular training correlate with greater recognition… More >

Raza Hasan^*, Shakeel Ahmad, Ismet Gocer, Zakirul Bhuiyan

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079708 - 08 May 2026

Abstract Maritime Domain Awareness (MDA) is critical for global security and economic stability, yet it is increasingly challenged by sophisticated adversarial tactics such as signal spoofing and “dark vessel” activities. Traditional surveillance systems, often reliant on single-sensor modalities, are ill-equipped to handle these deceptive behaviors. To address this, we propose the Multimodal Attention-based Fusion Transformer (MAFT), a novel deep learning architecture that integrates four distinct data modalities—Aerial imagery, Synthetic Aperture Radar (SAR), acoustic signatures, and Automatic Identification System (AIS) data—to achieve robust and interpretable maritime anomaly detection. A key contribution of our work is a principled… More > Graphic Abstract

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >

Haochen Bao¹, Lingyun Yuan^1,2,*, Tianyu Xie^1,2, Han Chen¹, Hui Dai¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069240 - 10 November 2025

Abstract In the age of big data, ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge. Traditional searchable encryption schemes face difficulties in handling complex semantic queries. Additionally, they typically rely on honest but curious cloud servers, which introduces the risk of repudiation. Furthermore, the combined operations of search and verification increase system load, thereby reducing performance. Traditional verification mechanisms, which rely on complex hash constructions, suffer from low verification efficiency. To address these challenges, this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification. Building on existing… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >

Gozde Karatas Baydogmus^*, Onder Demir

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3393-3406, 2025, DOI:10.32604/cmc.2025.062643 - 03 July 2025

Abstract Detection of Autism Spectrum Disorder (ASD) is a crucial area of research, representing a foundational aspect of psychological studies. The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years. Interdisciplinary efforts have further propelled research into detection methods. Consequently, this study aims to contribute to both the fields of psychology and computer science. Specifically, the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder. This study is structured into two distinct phases: data preprocessing and… More >

Kagiso Komane^1,*, Lucas Khoza², Fani Radebe¹

Journal of Cyber Security, Vol.7, pp. 79-108, 2025, DOI:10.32604/jcs.2025.059712 - 20 May 2025

Abstract Financial support, government support, cyber hygiene, and ongoing education and training as well as parental guidance and supervision are all essential components of cybersecurity awareness (CSA) identified in this study among the youth. It’s critical to realize that adequate funding is needed to effectively increase CSA, particularly among South African youth. Previous studies have demonstrated several ways to address inadequate CSA by utilizing various cybersecurity frameworks, ideas, and models. To increase CSA, this literature review seeks to emphasize the significance of integrating cybersecurity education throughout the entire school curriculum. This paper identified ethical issues, protection… More >

Longgang Zhao¹, Seok-Won Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4447-4476, 2025, DOI:10.32604/cmc.2025.063691 - 19 May 2025

Abstract This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. More >

Daniel Lesiba Letsoalo^1,*, Mahlatsi Venolia Semenya², Anastasia Julia Ngobe¹, Joy Katlego Hlokwe¹

Journal of Psychology in Africa, Vol.35, No.1, pp. 159-165, 2025, DOI:10.32604/jpa.2025.065764 - 30 April 2025

Abstract There has been an increase in mental health problems in Sub-Saharan Africa. Considering this, it is critical to track the region’s level of mental health literacy (MHL) to identify key mental health priorities and to direct the most effective interventions. The purpose of this study was to review the existing literature on MHL in sub-Saharan Africa. EBSCOhost (inclusive of Academic Search Ultimate, MEDLINE, APA PsycINFO, APA Psych Articles, and Global Health), CINAHL with full text, Wiley Online Library, Taylor and Francis Online Journals and Google Scholar databases were searched to retrieve relevant articles. The study… More >

Guojun Chen^*, Tao Cui, Yongjie Hou, Huihui Li

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3969-3987, 2024, DOI:10.32604/cmc.2024.056093 - 19 December 2024

Abstract Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance… More >