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  • Open Access

    ARTICLE

    DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

    Ziadoon K. Maseer1, Robiah Yusof1, Salama A. Mostafa2,*, Nazrulazhar Bahaman1, Omar Musa3, Bander Ali Saleh Al-rimy4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3945-3966, 2021, DOI:10.32604/cmc.2021.016074

    Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design… More >

  • Open Access

    ARTICLE

    Towards Machine Learning Based Intrusion Detection in IoT Networks

    Nahida Islam1, Fahiba Farhin1, Ishrat Sultana1, M. Shamim Kaiser1, Md. Sazzadur Rahman1, Mufti Mahmud2, A. S. M. Sanwar Hosen3, Gi Hwan Cho3,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1801-1821, 2021, DOI:10.32604/cmc.2021.018466

    Abstract The Internet of Things (IoT) integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience in many applications including healthcare, agriculture, supply chain, education, transportation and traffic monitoring, utility services etc. However, node heterogeneity raised security concern which is one of the most complicated issues on the IoT. Implementing security measures, including encryption, access control, and authentication for the IoT devices are ineffective in achieving security. In this paper, we identified various types of IoT threats and shallow (such as decision… More >

  • Open Access

    ARTICLE

    Towards a Dynamic Virtual IoT Network Based on User Requirements

    Faisal Mehmood1, Shabir Ahmad2,3, Israr Ullah1, Faisal Jamil1, DoHyeun Kim1,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2231-2244, 2021, DOI:10.32604/cmc.2021.017528

    Abstract The data being generated by the Internet of Things needs to be stored, monitored, and analyzed for maximum IoT resource utilization. Software Defined Networking has been extensively utilized to address issues such as heterogeneity and scalability. However, for small-scale IoT application, sometimes it is considered an inefficient approach. This paper proposes an alternate lightweight mechanism to the design and implementation of a dynamic virtual network based on user requirements. The key idea is to provide users a virtual interface that enables them to reconfigure the communication flow between the sensors and actuators at runtime. The throughput of the communication flow… More >

  • Open Access

    ARTICLE

    Secure Multifactor Remote Access User Authentication Framework for IoT Networks

    Mohammed Mujib Alshahrani*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3235-3254, 2021, DOI:10.32604/cmc.2021.015310

    Abstract The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require… More >

  • Open Access

    ARTICLE

    Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

    Arslan Musaddiq1, Rashid Ali2, Jin-Ghoo Choi1, Byung-Seo Kim3,*, Sung-Won Kim1

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 799-814, 2021, DOI:10.32604/cmc.2021.014751

    Abstract The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic… More >

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