Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.
Internet of Medical Things (IoMT) is a breakthrough technology in the transfer of medical data via a communication system. Wearable sensor devices collect patient data and transfer them through mobile internet, that is, the IoMT. Recently, the shift in paradigm from manual data storage to electronic health recording on fog, edge, and cloud computing has been noted. These advanced computing technologies have facilitated medical services with minimum cost and available conditions. However, the IoMT raises a high concern on network security and patient data privacy in the health care system. The main issue is the transmission of health data with high security in the fog computing model. In today's market, the best solution is blockchain technology. This technology provides high-end security and authentication in storing and transferring data. In this research, a blockchain-based fog computing model is proposed for the IoMT. The proposed technique embeds a block chain with the yet another consensus (YAC) protocol building security infrastructure into fog computing for storing and transferring IoMT data in the network. YAC is a consensus protocol that authenticates the input data in the block chain. In this scenario, the patients and their family members are allowed to access the data. The empirical outcome of the proposed technique indicates high reliability and security against dangerous threats. The major advantages of using the blockchain model are high transparency, good traceability, and high processing speed. The technique also exhibits high reliability and efficiency in accessing data with secure transmission. The proposed technique achieves 95% reliability in transferring a large number of files up to 10,000.
Industrial Internet of Things (IIoT) is an emerging field which connects digital equipment as well as services to physical systems. Intrusion detection systems (IDS) can be designed to protect the system from intrusions or attacks. In this view, this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection (HDL-MEID) technique for clustered IIoT environments. The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication. Primarily, the HDL-MEID technique designs a new chaotic mayfly optimization (CMFO) based clustering approach for the effective choice of the Cluster Heads (CH) and organize clusters. Moreover, equilibrium optimizer with hybrid convolutional neural network long short-term memory (HCNN-LSTM) based classification model is derived to identify the existence of the intrusions in the IIoT environment. Extensive experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different aspects. The experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.
Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses. The tremendous growth and evolution of Deep Learning (DL) techniques paved the way for increasing the accuracy of classification process. In this background, the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification (HSOHDL-PDC) model. The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs. In addition, SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance. Moreover, a hybrid Convolutional Neural Network-Long Short Term Memory (HCNN-LSTM) technique is also applied for identification and classification of phishing. The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes. The performance of the proposed HSOHDL-PDC model was validated using 6426 CMC, 2022, vol.73, no.3 different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.
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