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  • Open Access


    Novel Android Malware Detection Method Based on Multi-dimensional Hybrid Features Extraction and Analysis

    Yue Li1, Guangquan Xu2,3, Hequn Xian1,*, Longlong Rao3, Jiangang Shi4,*

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 637-647, 2019, DOI:10.31209/2019.100000118

    Abstract In order to prevent the spread of Android malware and protect privacy information from being compromised, this study proposes a novel multidimensional hybrid features extraction and analysis method for Android malware detection. This method is based primarily on a multidimensional hybrid features vector by extracting the information of permission requests, API calls, and runtime behaviors. The innovation of this study is to extract greater amounts of static and dynamic features information and combine them, that renders the features vector for training completer and more comprehensive. In addition, the feature selection algorithm is used to further More >

  • Open Access


    Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets

    Chunlai Du1, Shenghui Liu1, Lei Si2, Yanhui Guo2, *, Tong Jin1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091

    Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In More >

  • Open Access


    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… More >

  • Open Access


    Ameliorate Security by Introducing Security Server in Software Defined Network

    J. Vijila1, *, A. Albert Raj2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1077-1096, 2020, DOI:10.32604/cmc.2020.08534

    Abstract Software Defined Network (SDN) deals with huge data processing units which possess network management. However, due to centralization behavior ensuring security in SDN is the major concern. In this work to ensure security, a security server has been at its aid to check the vulnerability of the networks and to keep an eye on the packet according to the screening policies. A Secure Shell Connection (SSH) is established by the security server which does a frequent inspection of the network’s logs. Malware detection and the Intrusion Detection System policies are also incorporated in the server More >

  • Open Access


    MalDetect: A Structure of Encrypted Malware Traffic Detection

    Jiyuan Liu1, Yingzhi Zeng2, Jiangyong Shi2, Yuexiang Yang2,∗, Rui Wang3, Liangzhong He4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 721-739, 2019, DOI:10.32604/cmc.2019.05610

    Abstract Recently, TLS protocol has been widely used to secure the application data carried in network traffic. It becomes more difficult for attackers to decipher messages through capturing the traffic generated from communications of hosts. On the other hand, malwares adopt TLS protocol when accessing to internet, which makes most malware traffic detection methods, such as DPI (Deep Packet Inspection), ineffective. Some literatures use statistical method with extracting the observable data fields exposed in TLS connections to train machine learning classifiers so as to infer whether a traffic flow is malware or not. However, most of… More >

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