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  • Open Access

    ARTICLE

    An Immunization Scheme for Ransomware

    Jingping Song1, Qingyu Meng1, Chenke Luo2, Nitin Naik3, Jian Xu1, *

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1051-1061, 2020, DOI:10.32604/cmc.2020.010592

    Abstract In recent years, as the popularity of anonymous currencies such as Bitcoin has made the tracking of ransomware attackers more difficult, the amount of ransomware attacks against personal computers and enterprise production servers is increasing rapidly. The ransomware has a wide range of influence and spreads all over the world. It is affecting many industries including internet, education, medical care, traditional industry, etc. This paper uses the idea of virus immunity to design an immunization solution for ransomware viruses to solve the problems of traditional ransomware defense methods (such as anti-virus software, firewalls, etc.), which cannot meet the requirements of… More >

  • Open Access

    ARTICLE

    Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

    Bolun Chen1, 2, Guochang Zhu1, *, Min Ji1, Yongtao Yu1, Jianyang Zhao1, Wei Liu3

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583

    Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding… More >

  • Open Access

    ARTICLE

    Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

    Wenxi Han1, 2, Mingzhi Cheng3, *, Min Lei1, 2, Hanwen Xu2, Yu Yang1, 2, Lei Qian4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1025-1038, 2020, DOI:10.32604/cmc.2020.09815

    Abstract In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic… More >

  • Open Access

    ARTICLE

    New Three-Dimensional Assessment Model and Optimization of Acoustic Positioning System

    Lin Zhao1, Xiaobo Chen1, 2, *, Jianhua Cheng1, Lianhua Yu3, Chengcai Lv4, Jiuru Wang5

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1005-1023, 2020, DOI:10.32604/cmc.2020.010290

    Abstract This paper addresses the problem of assessing and optimizing the acoustic positioning system for underwater target localization with range measurement. We present a new three-dimensional assessment model to evaluate the optimal geometric beacon formation whether meets user requirements. For mathematical tractability, it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance, which is distance-dependent. Then, the relationship between DOP parameters and positioning accuracy can be derived by adopting dilution of precision (DOP) parameters in the assessment model. In addition, the optimal geometric beacon formation yielding the best performance… More >

  • Open Access

    ARTICLE

    Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory

    Hui Li1, Cailin Shi2, 3, Xin Liu2, 3, Aziguli Wulamu2, 3, *, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 987-1004, 2020, DOI:10.32604/cmc.2020.09812

    Abstract The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted… More >

  • Open Access

    ARTICLE

    Non-Exchangeable Error Compensation for Strapdown Inertial Navigation System in High Dynamic Environment

    Qi Wang1, 2, *, Changsong Yang2, 3, Shao’en Wu4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 973-986, 2020, DOI:10.32604/cmc.2020.07575

    Abstract Strapdown non-exchangeable error compensation technology in high dynamic environment is one of the key technologies of strapdown inertial navigation system. Mathematical platform is used in strapdown inertial navigation system instead of physical platform in traditional platform inertial navigation system, which improves reliability and reduces cost and volume of system. The maximum error source of attitude matrix solution is the non-exchangeable error of rotation due to the non-exchangeable of finite rotation of rigid bodies. The rotation non-exchangeable error reaches the maximum in coning motion, although it can be reduced by shortening the correction period and increasing the real-time calculation. The equivalent… More >

  • Open Access

    ARTICLE

    Resource Allocation in Edge-Computing Based Wireless Networks Based on Differential Game and Feedback Control

    Ruijie Lin1, Haitao Xu2, *, Meng Li3, Zhen Zhang4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 961-972, 2020, DOI:10.32604/cmc.2020.09686

    Abstract In this paper, we have proposed a differential game model to optimally solve the resource allocation problems in the edge-computing based wireless networks. In the proposed model, a wireless network with one cloud-computing center (CC) and lots of edge services providers (ESPs) is investigated. In order to provide users with higher services quality, the ESPs in the proposed wireless network should lease the computing resources from the CC and the CC can allocate its idle cloud computing resource to the ESPs. We will try to optimally allocate the edge computing resources between the ESPs and CC using the differential game… More >

  • Open Access

    ARTICLE

    A Network Traffic Classification Model Based on Metric Learning

    Mo Chen1, Xiaojuan Wang1, *, Mingshu He1, Lei Jin1, Khalid Javeed2, Xiaojun Wang3

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 941-959, 2020, DOI:10.32604/cmc.2020.09802

    Abstract Attacks on websites and network servers are among the most critical threats in network security. Network behavior identification is one of the most effective ways to identify malicious network intrusions. Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification. Traditional methods for network traffic classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. However, network traffic classification, which is required for network behavior identification, generally suffers from the problem of low accuracy even with the recently proposed deep learning models. To improve network… More >

  • Open Access

    ARTICLE

    GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

    Jinyuan Li1, Hao Li1, Guorong Cui1, Yan Kang1, *, Yang Hu1, Yingnan Zhou2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

    Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >

  • Open Access

    ARTICLE

    Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

    Ao Feng1, Zhengjie Gao1, *, Xinyu Song1, Ke Ke2, Tianhao Xu1, Xuelei Zhang1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 909-923, 2020, DOI:10.32604/cmc.2020.09913

    Abstract Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional… More >

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