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  • Open Access

    ARTICLE

    Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

    Khaled M. Alalayah1, Fatma S. Alrayes2, Jaber S. Alzahrani3, Khadija M. Alaidarous1, Ibrahim M. Alwayle1, Heba Mohsen4, Ibrahim Abdulrab Ahmed5, Mesfer Al Duhayyim6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3121-3139, 2023, DOI:10.32604/csse.2023.036352 - 03 April 2023

    Abstract With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further,… More >

  • Open Access

    ARTICLE

    Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

    Adepu Shravan Kumar, S. Srinivasan*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3607-3620, 2023, DOI:10.32604/iasc.2023.036647 - 15 March 2023

    Abstract At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without… More >

  • Open Access

    ARTICLE

    An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

    Samah Alshathri1, Ayman El-Sayed2, Walid El-Shafai3,4,*, Ezz El-Din Hemdan2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 819-834, 2023, DOI:10.32604/csse.2023.034095 - 20 January 2023

    Abstract Recently, the Internet of Things (IoT) has been used in various applications such as manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and productivity via an intelligent management console remotely. With the increased use of Industrial IoT (IIoT) applications, the risk of brutal cyber-attacks also increased. This leads researchers worldwide to work on developing effective Intrusion Detection Systems (IDS) for IoT infrastructure against any malicious activities. Therefore, this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure. A comprehensive evaluation examined on a new… More >

  • Open Access

    ARTICLE

    Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

    Xiaojuan Wang1, Zikui Lu1,*, Siyuan Sun2, Jingyue Wang1, Luona Song3, Merveille Nicolas4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750 - 22 September 2022

    Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for… More >

  • Open Access

    ARTICLE

    A Novel Approach for Network Vulnerability Analysis in IIoT

    K. Sudhakar*, S. Senthilkumar

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 263-277, 2023, DOI:10.32604/csse.2023.029680 - 16 August 2022

    Abstract Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the… More >

  • Open Access

    ARTICLE

    Anomaly Detection for Industrial Internet of Things Cyberattacks

    Rehab Alanazi*, Ahamed Aljuhani

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2361-2378, 2023, DOI:10.32604/csse.2023.026712 - 01 August 2022

    Abstract The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for Industrial Internet of Things Environment

    R. Gopi1, R. Sheeba2, K. Anguraj3, T. Chelladurai4, Haya Mesfer Alshahrani5, Nadhem Nemri6,*, Tarek Lamoudan7

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1567-1582, 2023, DOI:10.32604/csse.2023.025216 - 15 June 2022

    Abstract Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design… More >

  • Open Access

    ARTICLE

    Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic

    Jiale Cheng1, Zilong Jin1,2,*

    Journal on Internet of Things, Vol.4, No.3, pp. 183-195, 2022, DOI:10.32604/jiot.2022.042054 - 12 June 2023

    Abstract A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack More >

  • Open Access

    ARTICLE

    Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

    Zeyong Sun1, Guo Ran2, Zilong Jin1,3,*

    Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416 - 28 March 2023

    Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Threat Detection in Industrial Internet of Things Environment

    Fahad F. Alruwaili*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5809-5824, 2022, DOI:10.32604/cmc.2022.031613 - 28 July 2022

    Abstract Internet of Things (IoT) is one of the hottest research topics in recent years, thanks to its dynamic working mechanism that integrates physical and digital world into a single system. IoT technology, applied in industries, is termed as Industrial IoT (IIoT). IIoT has been found to be highly susceptible to attacks from adversaries, based on the difficulties observed in IIoT and its increased dependency upon internet and communication network. Intentional or accidental attacks on these approaches result in catastrophic effects like power outage, denial of vital health services, disruption to civil service, etc., Thus, there… More >

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