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  • Open Access

    ARTICLE

    Evaluation and Benchmarking of Cybersecurity DDoS Attacks Detection Models through the Integration of FWZIC and MABAC Methods

    Alaa Mahmood, İsa Avcı*

    Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

    Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

  • Open Access

    ARTICLE

    A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

    Ali Batouche1, Souham Meshoul2,*, Hadil Shaiba3, Mohamed Batouche2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1727-1752, 2025, DOI:10.32604/cmc.2025.063227 - 16 April 2025

    Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >

  • Open Access

    ARTICLE

    Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

    Long Cai, Ke Gu*, Jiaqi Lei

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 239-258, 2025, DOI:10.32604/cmc.2025.061377 - 26 March 2025

    Abstract Large-scale neural networks-based federated learning (FL) has gained public recognition for its effective capabilities in distributed training. Nonetheless, the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential attacks. Poisoning attacks turn into a major menace to federated learning on account of their concealed property and potent destructive force. By altering the local model during routine machine learning training, attackers can easily contaminate the global model. Traditional detection and aggregation solutions mitigate certain threats, but they are still insufficient to completely eliminate the influence generated by attackers. Therefore, federated… More >

  • Open Access

    ARTICLE

    A Novel Stacked Network Method for Enhancing the Performance of Side-Channel Attacks

    Zhicheng Yin1,2, Lang Li1,2,*, Yu Ou1,2

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1001-1022, 2025, DOI:10.32604/cmc.2025.060925 - 26 March 2025

    Abstract The adoption of deep learning-based side-channel analysis (DL-SCA) is crucial for leak detection in secure products. Many previous studies have applied this method to break targets protected with countermeasures. Despite the increasing number of studies, the problem of model overfitting. Recent research mainly focuses on exploring hyperparameters and network architectures, while offering limited insights into the effects of external factors on side-channel attacks, such as the number and type of models. This paper proposes a Side-channel Analysis method based on a Stacking ensemble, called Stacking-SCA. In our method, multiple models are deeply integrated. Through the… More >

  • Open Access

    ARTICLE

    Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

    Xiaoyin Yi1,2, Long Chen1,3,4,*, Jiacheng Huang1, Ning Yu1, Qian Huang5

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 157-175, 2025, DOI:10.32604/cmc.2025.059863 - 26 March 2025

    Abstract Transfer-based Adversarial Attacks (TAAs) can deceive a victim model even without prior knowledge. This is achieved by leveraging the property of adversarial examples. That is, when generated from a surrogate model, they retain their features if applied to other models due to their good transferability. However, adversarial examples often exhibit overfitting, as they are tailored to exploit the particular architecture and feature representation of source models. Consequently, when attempting black-box transfer attacks on different target models, their effectiveness is decreased. To solve this problem, this study proposes an approach based on a Regularized Constrained Feature More >

  • Open Access

    ARTICLE

    An Intrusion Detection System Based on HiTar-2024 Dataset Generation from LOG Files for Smart Industrial Internet-of-Things Environment

    Tarak Dhaouadi1, Hichem Mrabet1,2,*, Adeeb Alhomoud3, Abderrazak Jemai1,4

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4535-4554, 2025, DOI:10.32604/cmc.2025.060935 - 06 March 2025

    Abstract The increasing adoption of Industrial Internet of Things (IIoT) systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems (IDS) to be effective. However, existing datasets for IDS training often lack relevance to modern IIoT environments, limiting their applicability for research and development. To address the latter gap, this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems. As a consequence, that can be used by an IDS to detect imminent threats. Likewise, HiTar-2024 was generated using the AREZZO simulator, which replicates realistic smart manufacturing scenarios.… More >

  • Open Access

    ARTICLE

    Practical Adversarial Attacks Imperceptible to Humans in Visual Recognition

    Donghyeok Park1, Sumin Yeon2, Hyeon Seo2, Seok-Jun Buu2, Suwon Lee2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2725-2737, 2025, DOI:10.32604/cmes.2025.061732 - 03 March 2025

    Abstract Recent research on adversarial attacks has primarily focused on white-box attack techniques, with limited exploration of black-box attack methods. Furthermore, in many black-box research scenarios, it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts. Unfortunately, this disregard for the real-world practicality of attacks, particularly their potential for human detectability, has left a gap in the research landscape. Considering these limitations, our study focuses on using a similar color attack method, assuming access only to the output label, limiting the number of More >

  • Open Access

    ARTICLE

    Prioritizing Network-On-Chip Routers for Countermeasure Techniques against Flooding Denial-of-Service Attacks: A Fuzzy Multi-Criteria Decision-Making Approach

    Ahmed Abbas Jasim Al-Hchaimi1, Yousif Raad Muhsen2,3,*, Wisam Hazim Gwad4, Entisar Soliman Alkayal5, Riyadh Rahef Nuiaa Al Ogaili6, Zaid Abdi Alkareem Alyasseri7,8, Alhamzah Alnoor9

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2661-2689, 2025, DOI:10.32604/cmes.2025.061318 - 03 March 2025

    Abstract The implementation of Countermeasure Techniques (CTs) in the context of Network-On-Chip (NoC) based Multiprocessor System-On-Chip (MPSoC) routers against the Flooding Denial-of-Service Attack (F-DoSA) falls under Multi-Criteria Decision-Making (MCDM) due to the three main concerns, called: traffic variations, multiple evaluation criteria-based traffic features, and prioritization NoC routers as an alternative. In this study, we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios. Consequently, an MCDM approach is essential to address these emerging challenges. While the recent MCDM approach has some issues, such as uncertainty, this… More >

  • Open Access

    REVIEW

    Quick Response Code Security Attacks and Countermeasures: A Systematic Literature Review

    David Njuguna*, John Ndia

    Journal of Cyber Security, Vol.7, pp. 1-20, 2025, DOI:10.32604/jcs.2025.059398 - 18 February 2025

    Abstract A quick response code is a barcode that allows users to instantly access information via a digital device. Quick response codes store data as pixels in a square-shaped grid. QR codes are prone to cyber-attacks. This assault exploits human vulnerabilities, as users can scarcely discern what is concealed in the quick response code prior to usage. The aim of the study was to investigate Quick Response code attack types and the detection techniques. To achieve the objective, 50 relevant studies published between the year 2010 and 2024 were identified. The articles were obtained from the… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Detection and Selective Mitigation of Denial-of-Service Attacks in Wireless Sensor Networks

    Soyoung Joo#, So-Hyun Park#, Hye-Yeon Shim, Ye-Sol Oh, Il-Gu Lee*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2475-2494, 2025, DOI:10.32604/cmc.2025.058963 - 17 February 2025

    Abstract As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by More >

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