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  • Open Access

    ARTICLE

    Optimizing Network Intrusion Detection Performance with GNN-Based Feature Selection

    Hoon Ko1, Marek R. Ogiela2, Libor Mesicek3, Sangheon Kim4,*

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2985-2997, 2025, DOI:10.32604/cmc.2025.065885 - 23 September 2025

    Abstract The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the More >

  • Open Access

    ARTICLE

    Data-Driven Digital Evidence Analysis for the Forensic Investigation of the Electric Vehicle Charging Infrastructure

    Dong-Hyuk Shin1, Jae-Jun Ha1, Ieck-Chae Euom2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025

    Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >

  • Open Access

    ARTICLE

    Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

    Dahyeon Kim1, Namgi Kim2, Junho Ahn2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3867-3889, 2024, DOI:10.32604/cmc.2024.046871 - 26 March 2024

    Abstract This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities, generating applicable guidelines based on real-world software. The existing analysis of software security vulnerabilities often focuses on specific features or modules. This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software. The key novelty lies in overcoming the constraints of partial approaches. The proposed framework utilizes data from various sources to create a comprehensive functionality profile, facilitating the derivation of real-world security guidelines.… More >

  • Open Access

    ARTICLE

    A Novel Approach for Network Vulnerability Analysis in IIoT

    K. Sudhakar*, S. Senthilkumar

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 263-277, 2023, DOI:10.32604/csse.2023.029680 - 16 August 2022

    Abstract Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the… More >

  • Open Access

    Vulnerability Analysis of MEGA Encryption Mechanism

    Qingbing Ji1,2,*, Zhihong Rao1,2, Lvlin Ni2, Wei Zhao2, Jing Fu3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 817-829, 2022, DOI:10.32604/cmc.2022.026949 - 18 May 2022

    Abstract MEGA is an end-to-end encrypted cloud storage platform controlled by users. Moreover, the communication between MEGA client and server is carried out under the protection of Transport Layer Security (TLS) encryption, it is difficult to intercept the key data packets in the process of MEGA registration, login, file data upload, and download. These characteristics of MEGA have brought great difficulties to its forensics. This paper presents a method to attack MEGA to provide an effective method for MEGA’s forensics. By debugging the open-source code of MEGA and analyzing the security white paper published, this paper More >

  • Open Access

    ARTICLE

    Seismic Vulnerability Analysis of Single-Story Reinforced Concrete Industrial Buildings with Seismic Fortification

    Jieping Liu1, Lingxin Zhang1,*, Haohao Zhang2, Tao Liu1

    Structural Durability & Health Monitoring, Vol.13, No.2, pp. 123-142, 2019, DOI:10.32604/sdhm.2019.04486

    Abstract As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed… More >

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