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  • Open Access

    ARTICLE

    FSMMTD: A Feature Subset-Based Malicious Traffic Detection Method

    Xuan Wu1, Yafei Song1, Xiaodan Wang1,*, 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 >

  • Open Access

    ARTICLE

    Research on SQL Injection Detection Technology Based on Content Matching and Deep Learning

    Yuqi Chen1,2, Guangjun Liang1,2,3,*, Qun Wang1,2,3

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1145-1167, 2025, DOI:10.32604/cmc.2025.063319 - 09 June 2025

    Abstract Structured Query Language (SQL) injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm. Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks. This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection. Features are extracted through content matching, effectively avoiding the loss of valid information, and an improved deep learning model is employed to enhance the detection effect of SQL injections. Considering that grammar More >

  • Open Access

    REVIEW

    Edge-Fog Enhanced Post-Quantum Network Security: Applications, Challenges and Solutions

    Seo Yeon Moon1, Byung Hyun Jo1, Abir El Azzaoui1, Sushil Kumar Singh2, Jong Hyuk Park1,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 25-55, 2025, DOI:10.32604/cmc.2025.062966 - 09 June 2025

    Abstract With the rapid advancement of ICT and IoT technologies, the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency. However, these technologies face critical security challenges, exacerbated by the emergence of quantum computing, which threatens traditional encryption methods. The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust, quantum-resistant security solutions. To address these challenges, researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography, which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality More >

  • Open Access

    REVIEW

    An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

    Uddagiri Sirisha1,*, Chanumolu Kiran Kumar2, Sujatha Canavoy Narahari3, Parvathaneni Naga Srinivasu4,5,6

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025

    Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >

  • Open Access

    ARTICLE

    Enhancing Network Security: Leveraging Machine Learning for Integrated Protection and Intrusion Detection

    Nada Mohammed Murad1, Adnan Yousif Dawod2, Saadaldeen Rashid Ahmed3,4,*, Ravi Sekhar5, Pritesh Shah5

    Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624 - 10 January 2025

    Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

  • Open Access

    ARTICLE

    An Intelligent Security Service Optimization Method Based on Knowledge Base

    Xianju Gao*, Huachun Zhou, Weilin Wang, Jingfu Yan

    Computer Systems Science and Engineering, Vol.49, pp. 19-48, 2025, DOI:10.32604/csse.2024.058327 - 03 January 2025

    Abstract The network security knowledge base standardizes and integrates network security data, providing a reliable foundation for real-time network security protection solutions. However, current research on network security knowledge bases mainly focuses on their construction, while the potential to optimize intelligent security services for real-time network security protection requires further exploration. Therefore, how to effectively utilize the vast amount of historical knowledge in the field of network security and establish a feedback mechanism to update it in real time, thereby enhancing the detection capability of security services against malicious traffic, has become an important issue. Our… More >

  • Open Access

    ARTICLE

    Robust Network Security: A Deep Learning Approach to Intrusion Detection in IoT

    Ammar Odeh*, Anas Abu Taleb

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

    Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >

  • Open Access

    REVIEW

    Enhancing Cyber Security through Artificial Intelligence and Machine Learning: A Literature Review

    Carlos Merlano*

    Journal of Cyber Security, Vol.6, pp. 89-116, 2024, DOI:10.32604/jcs.2024.056164 - 06 December 2024

    Abstract The constantly increasing degree and frequency of cyber threats require the emergence of flexible and intelligent approaches to systems’ protection. Despite the calls for the use of artificial intelligence (AI) and machine learning (ML) in strengthening cyber security, there needs to be more literature on an integrated view of the application areas, open issues or trends in AI and ML for cyber security. Based on 90 studies, in the following literature review, the author categorizes and systematically analyzes the current research field to fill this gap. The review evidences that, in contrast to rigid rule-based… More >

  • Open Access

    REVIEW

    A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

    Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

  • Open Access

    ARTICLE

    Adaptive Update Distribution Estimation under Probability Byzantine Attack

    Gang Long, Zhaoxin Zhang*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1667-1685, 2024, DOI:10.32604/cmc.2024.052082 - 15 October 2024

    Abstract The secure and normal operation of distributed networks is crucial for accurate parameter estimation. However, distributed networks are frequently susceptible to Byzantine attacks. Considering real-life scenarios, this paper investigates a probability Byzantine (PB) attack, utilizing a Bernoulli distribution to simulate the attack probability. Historically, additional detection mechanisms are used to mitigate such attacks, leading to increased energy consumption and burdens on distributed nodes, consequently diminishing operational efficiency. Differing from these approaches, an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks. In the proposed algorithm, a penalty strategy is initially More >

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