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  • Open Access

    ARTICLE

    BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

    Khlood Shinan1,2, Khalid Alsubhi2, M. Usman Ashraf3,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 693-714, 2023, DOI:10.32604/cmc.2023.031641

    Abstract The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study is to build a botnet… More >

  • Open Access

    ARTICLE

    An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

    G. Naveen Sundar1, Stalin Selvaraj2, D. Narmadha1, K. Martin Sagayam3, A. Amir Anton Jone3, Ayman A. Aly4, Dac-Nhuong Le5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 31-46, 2022, DOI:10.32604/cmes.2022.019914

    Abstract Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research, we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top… More >

  • Open Access

    ARTICLE

    Network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

    Qian Sun1,2, Fengbo Yang1,2, Xiaoyi Wang2,3, Jing Li4,*, Jiping Xu1,2, Huiyan Zhang1,2, Li Wang1,2, Jiabin Yu1,2, Xiao Peng1,2, Ruichao Wang5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3021-3038, 2022, DOI:10.32604/cmc.2022.029367

    Abstract Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that… More >

  • Open Access

    ARTICLE

    Seed-Oriented Local Community Detection Based on Influence Spreading

    Shenglong Wang1,*, Jing Yang1,*, Xiaoyu Ding2, Jianpei Zhang1, Meng Zhao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 215-249, 2022, DOI:10.32604/cmes.2022.018050

    Abstract In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters, respectively. To… More >

  • Open Access

    ARTICLE

    Complex Network Formation and Analysis of Online Social Media Systems

    Hafiz Abid Mahmood Malik*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1737-1750, 2022, DOI:10.32604/cmes.2022.018015

    Abstract To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has been formalized and results are… More >

  • Open Access

    ARTICLE

    CGraM: Enhanced Algorithm for Community Detection in Social Networks

    Kalaichelvi Nallusamy*, K. S. Easwarakumar

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189

    Abstract Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a… More >

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