Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (373)
  • Open Access


    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

  • Open Access


    Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

    Amerah Alabrah*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, DOI:10.32604/cmc.2024.048528

    Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However,… More >

  • Open Access


    Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

    Widad Elbakri1, Maheyzah Md. Siraj1,*, Bander Ali Saleh Al-rimy1, Sultan Noman Qasem2, Tawfik Al-Hadhrami3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105

    Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

  • Open Access


    A Novel Numerical Method for Simulating Boiling Heat Transfer of Nanofluids

    Yang Cao*, Xuhui Meng

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 583-595, 2024, DOI:10.32604/fhmt.2024.049111

    Abstract In this paper, a new approach called the Eulerian species method was proposed for simulating the convective and/or boiling heat transfer of nanofluids. The movement of nanoparticles in nanofluids is tracked by the species transport equation, and the boiling process of nanofluids is computed by the Eulerian multiphase method coupled with the RPI boiling model. The validity of the species transport equation for simulating nanoparticles movement was verified by conducting a simulation of nanofluids convective heat transfer. Simulation results of boiling heat transfer of nanofluids were obtained by using the commercial CFD software ANSYS Fluent More >

  • Open Access


    Numerical Examination of a Cavity Containing Nanofluid with an Upper Oscillating Wall and Baffle

    Kadhum Audaa Jehhef1, Ali J. Ali2, Salah H. Abid Aun1, Akram H. Abed3,*

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 557-581, 2024, DOI:10.32604/fhmt.2024.047814

    Abstract The cavity with lid-driven is greatly used in mixing, coating, and drying applications and is a substantial issue in the study of thermal performance rate and fluid field. A numerical approach is presented to study the thermal distribution and passage of fluid in a lid-driven cavity with an upper oscillating surface and an attached baffle. The walls of a cavity at the left and right were maintained at 350 and 293 K, respectively. The upper oscillating surface was equipped with a variable height to baffle to increase the convection of the three kinds of TiO,… More >

  • Open Access


    RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3175-3192, 2024, DOI:10.32604/cmc.2023.042873

    Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that… More >

  • Open Access


    A Wind Power Prediction Framework for Distributed Power Grids

    Bin Chen1, Ziyang Li1, Shipeng Li1, Qingzhou Zhao1, Xingdou Liu2,*

    Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374

    Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on More >

  • Open Access


    A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

    Wenlu Ji*, Teng Tu, Nan Ma

    Energy Engineering, Vol.121, No.5, pp. 1223-1243, 2024, DOI:10.32604/ee.2024.046112

    Abstract To improve the resilience of a distribution system against extreme weather, a fuel-based distributed generator (DG) allocation model is proposed in this study. In this model, the DGs are placed at the planning stage. When an extreme event occurs, the controllable generators form temporary microgrids (MGs) to restore the load maximally. Simultaneously, a demand response program (DRP) mitigates the imbalance between the power supply and demand during extreme events. To cope with the fault uncertainty, a robust optimization (RO) method is applied to reduce the long-term investment and short-term operation costs. The optimization is formulated More >

  • Open Access


    Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

    Khaled Soliman1,*, Mohamed Sobh2, Ayman M. Bahaa-Eldin2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

    Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse… More >

  • Open Access


    Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

    Haojie Lian1, Jiaqi Wang1, Leilei Chen2,*, Shengze Li3, Ruochen Cao4, Qingyuan Hu5, Peiyun Zhao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549

    Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of More >

Displaying 1-10 on page 1 of 373. Per Page