Jeongeun Ryu¹, Riyeong Kim², Soomin Lee¹, Sumin Kim¹, Hyunwoo Choi^1,2, Seongmin Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074871 - 12 January 2026

Abstract As containerized environments become increasingly prevalent in cloud-native infrastructures, the need for effective monitoring and detection of malicious behaviors has become critical. Malicious containers pose significant risks by exploiting shared host resources, enabling privilege escalation, or launching large-scale attacks such as cryptomining and botnet activities. Therefore, developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems. To this end, we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter (eBPF) to monitor container activities directly within the Linux kernel. The framework simultaneously collects flow-based network… More >

Seong-Su Yoon, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2683-2706, 2025, DOI:10.32604/cmes.2025.071577 - 26 November 2025

Abstract With the continuous expansion of digital infrastructures, malicious behaviors in host systems have become increasingly sophisticated, often spanning multiple processes and employing obfuscation techniques to evade detection. Audit logs, such as Sysmon, offer valuable insights; however, existing approaches typically flatten event sequences or rely on generic graph models, thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks. This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional (2D) spatio-temporal representation, where process hierarchy is modeled as the spatial axis and event chronology as the More >

Suhyeon Lee¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2657-2682, 2025, DOI:10.32604/cmes.2025.070627 - 26 November 2025

Abstract The open nature and heterogeneous architecture of Open Radio Access Network (Open RAN) undermine the consistency of security policies and broaden the attack surface, thereby increasing the risk of security vulnerabilities. The dynamic nature of network performance and traffic patterns in Open RAN necessitates advanced detection models that can overcome the constraints of traditional techniques and adapt to evolving behaviors. This study presents a methodology for effectively detecting malicious traffic in Open RAN by utilizing an Artificial-Intelligence/Machine-Learning (AI/ML) Framework. A hybrid Transformer–Convolutional-Neural-Network (Transformer-CNN) ensemble model is employed for anomaly detection. The proposed model generates final More >

Raidizon Mercedes, Eric Eidelman, Michael Mawhorter, Max Yudovich, Alireza Aminsharifi^*

Canadian Journal of Urology, Vol.32, No.5, pp. 515-520, 2025, DOI:10.32604/cju.2025.067973 - 30 October 2025

Abstract Background: Spontaneous rupture of the urinary bladder (SRUB) is a rare condition characterized by bladder rupture without any trauma or previous instrumentation. Diagnosing SRUB can be challenging, leading to potential delays in treatment and significant morbidity. Case description: We present a case of a 75-year-old male with a complex medical history, including atrial fibrillation, systemic lupus erythematosus, antiphospholipid syndrome, and chronic anticoagulation, who developed sudden onset gross hematuria and abdominal pain following bilateral internal iliac artery angioembolization for a spontaneous pelvic hematoma in the setting of supratherapeutic anticoagulation. Extraperitoneal bladder perforation was confirmed by CT cystogram.… More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Badraddin Alturki³, Ayman Yafoz¹, Ali Alqahtani⁴, Raed Alsini¹, Sami Saeed Binyamin⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1117-1140, 2025, DOI:10.32604/cmes.2025.070745 - 30 October 2025

Abstract QR codes are widely used in applications such as information sharing, advertising, and digital payments. However, their growing adoption has made them attractive targets for malicious activities, including malware distribution and phishing attacks. Traditional detection approaches rely on URL analysis or image-based feature extraction, which may introduce significant computational overhead and limit real-time applicability, and their performance often depends on the quality of extracted features. Previous studies in malicious detection do not fully focus on QR code security when combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This research proposes a deep learning… More >

Binu Sudhakaran Pillai¹, Raghavendra Kulkarni², Venkata Satya Suresh kumar Kondeti², Surendran Rajendran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1141-1166, 2025, DOI:10.32604/cmes.2025.070348 - 30 October 2025

Abstract Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new… More >

Xiaojun Xu^1,#, Hui Lu^2,#, Mengni Du³, Yang Wang^1,4, Mingyan Liu², Lei Qian^1,5, Chunyan Shan¹, Jianan Xu⁶, Yanqiu Yu⁷, Guohua Zhang⁴, Anise M. S. Wu^8,9, Joseph T. F. Lau^1,4,10,*, Deborah Baofeng Wang^1,*

International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1423-1437, 2025, DOI:10.32604/ijmhp.2025.068840 - 31 October 2025

Abstract Background: Adolescent depression and school refusal (SR) are prevalent and important global concerns that need to be understood and addressed. Cross-sectional associations have been reported but prospective relationships between them remain unclear. This longitudinal study investigated the bidirectional relationships between these two problems among Chinese adolescents. Methods: A longitudinal study was conducted in Taizhou, China, surveying students of three junior high schools, three senior high schools, and one vocational high school. A total of 3882 students completed the questionnaire at baseline (T1); 3167 of them completed an identical follow-up questionnaire after 6 months (T2). Depression… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Abdulaziz Yagoub Barnawi²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3213-3250, 2025, DOI:10.32604/cmes.2025.066589 - 30 September 2025

Abstract Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks (WSNs), especially in security-critical, time-sensitive applications. However, most existing protocols degrade substantially under malicious interference. We introduce iSTSP, an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust, precise synchronization even in hostile environments: (1) trust preprocessing that filters node participation using behavioral trust scoring; (2) anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time; (3) reliability-weighted consensus that prioritizes high-trust nodes during time aggregation; and (4) convergence-optimized synchronization… More >

Xuan Wu¹, Yafei Song¹, Xiaodan Wang^1,*, Peng Wang¹, Qian Xiang²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1279-1305, 2025, DOI:10.32604/cmc.2025.064471 - 09 June 2025

Abstract With the growth of the Internet of Things (IoT) comes a flood of malicious traffic in the IoT, intensifying the challenges of network security. Traditional models operate with independent layers, limiting their effectiveness in addressing these challenges. To address this issue, we propose a cross-layer cooperative Feature Subset-Based Malicious Traffic Detection (FSMMTD) model for detecting malicious traffic. Our approach begins by applying an enhanced random forest method to adaptively filter and retain highly discriminative first-layer features. These processed features are then input into an improved state-space model that integrates the strengths of recurrent neural networks… More >