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  • Open Access


    Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

    Ziqiao Zhou1, Tianyang Zhou1,*, Jinghao Xu2, Junhu Zhu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553

    Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

  • Open Access


    Oscillatory Dynamics of a Spherical Solid in a Liquid in an Axisymmetric Variable Cross Section Channel

    Ivan Karpunin*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1219-1232, 2024, DOI:10.32604/fdmp.2024.051062

    Abstract The dynamics of a solid spherical body in an oscillating liquid flow in a vertical axisymmetric channel of variable cross section is experimentally studied. It is shown that the oscillating liquid leads to the generation of intense averaged flows in each of the channel segments. The intensity and direction of these flows depend on the dimensionless oscillating frequency. In the region of studied frequencies, the dynamics of the considered body is examined when the primary vortices emerging in the flow occupy the whole region in each segment. For a fixed frequency, an increase in the… More >

  • Open Access


    A Novel Method for Determining the Void Fraction in Gas-Liquid Multi-Phase Systems Using a Dynamic Conductivity Probe

    Xiaochu Luo1, Xiaobing Qi2, Zhao Luo3, Zhonghao Li4, Ruiquan Liao1, Xingkai Zhang1,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1233-1249, 2024, DOI:10.32604/fdmp.2023.045737

    Abstract Conventional conductivity methods for measuring the void fraction in gas-liquid multiphase systems are typically affected by accuracy problems due to the presence of fluid flow and salinity. This study presents a novel approach for determining the void fraction based on a reciprocating dynamic conductivity probe used to measure the liquid film thickness under forced annular-flow conditions. The measurement system comprises a cyclone, a conductivity probe, a probe reciprocating device, and a data acquisition and processing system. This method ensures that the flow pattern is adjusted to a forced annular flow, thereby minimizing the influence of More >

  • Open Access


    SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

    Jiahui Liu1,2, Lang Li1,2,*, Di Li1,2, Yu Ou1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4641-4657, 2024, DOI:10.32604/cmc.2024.051613

    Abstract Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes… More >

  • Open Access


    A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness

    Yucheng Liang1,2,*, Ke Niu1,2,*, Yingnan Zhang1,2, Yifei Meng1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5143-5174, 2024, DOI:10.32604/cmc.2024.051364

    Abstract The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos. Grounded in the H.264 video coding standard, the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel. Subsequently, it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information, enabling successful watermark extraction and lossless recovery of the original video content. Experimental results demonstrate the algorithm’s strong imperceptibility, with… More >

  • Open Access


    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

    Djeldjli Halima1,*, Benatiallah Djelloul1, Ghasri Mehdi2, Tanougast Camel3, Benatiallah Ali4, Benabdelkrim Bouchra1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002

    Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

  • 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


    Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

    Xin Xiong1, Jing Zhang1, Sanli Yi1, Chunwu Wang2, Ruixiang Liu3, Jianfeng He1,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528

    Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

  • Open Access


    A Research Progress of CO2-Responsive Plugging Channeling Gels

    Yang Xiong1,2, Jianxin Liu1,2,*, Xianhao Yi2, Bangyan Xiao2, Dan Wu2, Biao Wu2, Chunyu Gao2

    Energy Engineering, Vol.121, No.7, pp. 1759-1780, 2024, DOI:10.32604/ee.2024.048536

    Abstract In the heterogeneous reservoirs, CO2 flooding easily leads to CO2 gas channeling, which can seriously affect sweeping efficiency and reduce oil recovery. After thoroughly investigating the advantages and shortcomings of various CO2 plugging technologies, this paper focuses on the feasibility of improving conventional water-alternating gas (WAG) through CO2-responsive gel materials. Based on the different chemical reaction mechanisms between the unique chemical structure and CO2, changes in the material’s physical and chemical properties can respond to CO2. The feasibility of utilizing these property changes for CO2-responsive plugging is explored. Various CO2-responsive gels and gel nanoparticles have been extensively researched in More >

  • Open Access


    Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

    Feng Zhao, Guangdi Liu*, Xiaoqiang Chen, Ying Wang

    Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

    Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

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