Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Adam Khalid¹, Anazida Zainal¹, Fuad A. Ghaleb², Bander Ali Saleh Al-rimy³, Yussuf Ahmed^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5665-5691, 2025, DOI:10.32604/cmc.2025.063451 - 30 July 2025

Abstract Advanced Persistent Threats (APTs) represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour, long-term persistence, and ability to bypass traditional detection systems. The complexity of real-world network data poses significant challenges in detection. Machine learning models have shown promise in detecting APTs; however, their performance often suffers when trained on large datasets with redundant or irrelevant features. This study presents a novel, hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data. It combines Mutual Information (MI), Symmetric… More >

Feng Zhu^*, Kailin Wu, Jie Ding

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2573-2598, 2025, DOI:10.32604/cmc.2025.062007 - 16 April 2025

Abstract With the widespread application of Internet of Things (IoT) technology, the processing of massive real-time streaming data poses significant challenges to the computational and data-processing capabilities of systems. Although distributed streaming data processing frameworks such as Apache Flink and Apache Spark Streaming provide solutions, meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem. To address this, the study proposes a formal modeling approach based on Performance Evaluation Process Algebra (PEPA), which abstracts the core components and interactions of cloud-based distributed streaming data processing systems. Additionally, a generic service… More >

Ali Alqarni^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >

Dunya Ahmed Alkurdi^1,2,*, Mesut Cevik², Abdurrahim Akgundogdu³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4285-4305, 2024, DOI:10.32604/cmc.2024.057029 - 19 December 2024

Abstract Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant… More >

Zhan Shi^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530 - 18 July 2024

Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

Aizaz Ali¹, Maqbool Khan^1,2, Khalil Khan³, Rehan Ullah Khan⁴, Abdulrahman Aloraini^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712 - 25 April 2024

Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features,… More >

Abdullah Alsaleh^1,2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 431-449, 2024, DOI:10.32604/csse.2023.043107 - 19 March 2024

Abstract With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the… More >

Ze Xu, Sanxing Cao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 861-881, 2023, DOI:10.32604/cmes.2023.025159 - 05 January 2023

Abstract Multi-Source data plays an important role in the evolution of media convergence. Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data. However, it also faces serious problems in terms of protecting user and data privacy. Many privacy protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two flaws: 1) the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;… More > Graphic Abstract

Ying Du^1,2, Shuo Zhang^1,2, Pu Cheng^3,*, Rita Yi Man Li⁴, Xiao-Guang Yue^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1965-1979, 2023, DOI:10.32604/cmes.2023.024871 - 23 November 2022

Abstract Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance. To solve the task scheduling problem for remote sensing data processing in cloud computing, this paper proposes a workflow task scheduling algorithm---Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning (WDRL). The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem. Then, the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method. Finally, combine the advantages of reinforcement learning and deep neural networks to minimize make-time More >