Bing Zhang, Wenqi Shi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.067470 - 09 December 2025

Abstract To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments, which results from anomaly detection mechanisms in location-based service (LBS) applications, this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis. The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns. First, we design an automated data extraction algorithm that recognizes graphical user interface (GUI) elements to collect spatio-temporal behavior data. Then, by analyzing the automatically collected user data, we identify normal users’ spatio-temporal patterns and extract their… More >

Jong-Yih Kuo¹, Ping-Feng Wang^2,*, Ti-Feng Hsieh^1,*, Cheng-Hsuan Kuo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3133-3166, 2025, DOI:10.32604/cmes.2025.064104 - 30 June 2025

Abstract In recent years, cyber threats have escalated across diverse sectors, with cybercrime syndicates increasingly exploiting system vulnerabilities. Traditional passive defense mechanisms have proven insufficient, particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets. According to Trend Micro, there has been a substantial increase in Linux-targeted malware, with ransomware attacks on Linux surpassing those on macOS. This alarming trend underscores the need for detection strategies specifically designed for Linux environments. To address this challenge, this study proposes a comprehensive malware detection framework tailored for Linux systems, integrating dynamic behavioral analysis with the… More >

Lingyun Xiang^1,2,3, Guoqing Guo², Qian Li⁴, Chengzhang Zhu^5,*, Jiuren Chen⁶, Haoliang Ma²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1375-1390, 2020, DOI:10.32604/iasc.2020.013382 - 24 December 2020

Abstract Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and More >

Pengshou Xie¹, Guoqiang Ma^{1, *}, Tao Feng¹, Yan Yan^{1, 2}, Xueming Han¹

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1127-1137, 2020, DOI:10.32604/cmc.2020.09695 - 10 June 2020

Abstract Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet of Vehicles (IoV) by destroying routing or data. To this end, some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication, data, energy, etc., to detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust level of a vehicle node only by message forwarding, data consistency, and energy sufficiency. In order to resolve these problems, a novel mechanism and a new trust calculating model is proposed in this… More >