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 of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.
Recent advancements in hardware and networking technologies have resulted in a large growth in the number of Internet of Things (IoT) devices connected to the Internet, which is likely to continue growing in the coming years. Traditional security solutions are insufficiently suited to the IoT context due to the restrictions and diversity of the resources available to objects. Security techniques such as intrusion detection and authentication are considered to be effective. Additionally, the decentralised and distributed nature of Blockchain technology makes it an excellent solution for overcoming the security issue. This paper proposes a chaotic bird swarm algorithm (CBSA)-based clustering technique based on an optimum deep belief network (ODBN) and Blockchain technology for secure authentication in an IoT setting. The CBSA-ODBN technique creates a clustering algorithm that utilises CBSA to pick cluster heads (CHs). The ODBN model is then utilised to identify the network, and the learning rate of the Deep Belief Network (DBN) model is optimally adjusted using the flower pollination technique (FPA). The suggested concept creates a layered security network paradigm for the Internet of Things using blockchain technology. Numerous simulations are run, with the outcomes analysed using a variety of measures, including detection rate, packet delivery ratio, energy usage, end-to-end latency, and processing cost. A careful comparison of the suggested model's performance to recently developed methodologies demonstrated the proposed model's superior performance.
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