Xinli Hao¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073155 - 12 January 2026

Abstract Topological information is very important for understanding different types of online web services, in particular, for online social networks (OSNs). People leverage such information for various applications, such as social relationship modeling, community detection, user profiling, and user behavior prediction. However, the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users. Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services. In this paper, we explore how to defend against topological information probing for online web services,… More >

Karim Boudjebbour^1,2, Abdelkader Belkhir¹, Hamza Kheddar^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071532 - 12 January 2026

Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >

Maryan Rizinski^1,2,*, Dimitar Trajanov^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-34, 2026, DOI:10.32604/cmc.2025.069678 - 10 November 2025

Abstract Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically, it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, robo-advisory, and regulatory compliance (RegTech). The review focuses on advanced agent-based methodologies, including reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely… More >

Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >

Faareh Ahmed¹, Babar Mansoor², Muhammad Awais Javed³, Abdul Khader Jilani Saudagar^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3783-3804, 2025, DOI:10.32604/cmes.2025.069430 - 30 September 2025

Abstract Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content. However, retrieving infotainment data from remote servers often introduces high delays, degrading the Quality of Service (QoS). To overcome this, caching frequently requested content at fog-enabled Road Side Units (RSUs) reduces communication latency. Yet, the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity. This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction. The scheme is evaluated against Intelligent Content Caching (ICC) and Random Caching More >

Kailai Zhou^*, Weikang Xia, Jiatai Wang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3217-3240, 2025, DOI:10.32604/cmc.2025.065334 - 03 July 2025

Abstract Ride-hailing (e.g., DiDi and Uber) has become an important tool for modern urban mobility. To improve the utilization efficiency of ride-hailing vehicles, a novel query method, called Approachable k-nearest neighbor (A-kNN), has recently been proposed in the industry. Unlike traditional kNN queries, A-kNN considers not only the road network distance but also the availability status of vehicles. In this context, even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location. The V-Tree-based query method, due to its structural characteristics, is capable of efficiently finding k-nearest moving objects within… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >

Xiongwei Cui, Yunchao Wang, Qiang Wei^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4939-4959, 2025, DOI:10.32604/cmc.2025.063592 - 19 May 2025

Abstract The development of the Internet of Things (IoT) has brought convenience to people’s lives, but it also introduces significant security risks. Due to the limitations of IoT devices themselves and the challenges of re-hosting technology, existing fuzzing for IoT devices is mainly conducted through black-box methods, which lack effective execution feedback and are blind. Meanwhile, the existing static methods mainly rely on taint analysis, which has high overhead and high false alarm rates. We propose a new directed fuzz testing method for detecting bugs in web service programs of IoT devices, which can test IoT… More >