IoT security: Review, blockchain solutions, and open challenges

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Current research on Internet of Things (IoT) security: A survey

The blockchain technology has revolutionized the digital currency space with the pioneering cryptocurrency platform named Bitcoin. From an abstract perspective, a blockchain is a distributed ledger capable of maintaining an immutable log of transactions happening in a network. In recent years, this technology has attracted significant scientific interest in research areas beyond the financial sector, one of them being the Internet of Things (IoT). In this context, the blockchain is seen as the missing link toward building a truly decentralized, trustless, and secure environment for the IoT and, in this survey, we aim to shape a coherent and comprehensive picture of the current state-of-the-art efforts in this direction. We start with fundamental working principles of blockchains and how blockchain-based systems achieve the characteristics of decentralization, security, and auditability. From there, we build our narrative on the challenges posed by the current centralized IoT models, followed by recent advances made both in industry and research to solve these challenges and effectively use blockchains to provide a decentralized, secure medium for the IoT.

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Applications of Blockchains in the Internet of Things: A Comprehensive Survey

A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT

Internet of Things security

There has been a rise in the demand for blockchain-based smart contract development platforms and language implementations. On the other hand, smart contracts and blockchain applications are generated using non-standard software life cycles, which means that, for example, distributed applications are rarely updated, or bugs are fully addressed by releasing a newer version, leading to security flaws and challenges for users to adopt the technology. Smart contracts have gained significant attention due to their potential to automate and secure various transactions in diverse domains. However, the increasing adoption of smart contracts has also raised concerns about security vulnerabilities and potential risks. In this paper, an overview of smart contracts was discussed in detail. It further distinguished and compared smart contracts security with conventional security regarding security, privacy, communication channel, etc. Different platforms for smart contracts, such as Bitcoin, Ethereum, Counterparty, Stellar, Monax, and Lisk, are also discussed in this paper. Some proposed techniques are used in different areas for handling security threats in smart contracts. In addition, a taxonomy of the smart contracts security application was proposed, which attempts to solve some of the flaws and inadequacies in smart contracts. The study also provides a comprehensive smart contracts security scenario with different techniques. Lastly, the possible attacks posed by threats and vulnerabilities of the smart contracts are provided. The security threats and vulnerabilities addressed in this study are unique to smart contracts.

Smart Contracts Security Application and Challenges: A Review

Backpropagation neural networks are commonly utilized to solve complicated issues in various disciplines. However, optimizing their settings remains a significant task. Traditional gradient-based optimization methods, such as stochastic gradient descent (SGD), often exhibit slow convergence and hyperparameter sensitivity. An adaptive stochastic conjugate gradient (ASCG) optimization strategy for backpropagation neural networks is proposed in this study. ASCG combines the advantages of stochastic optimization and conjugate gradient techniques to increase training efficiency and convergence speed. Based on the observed gradients, the algorithm adaptively calculates the learning rate and search direction at each iteration, allowing for quicker convergence and greater generalization. Experimental findings on benchmark datasets show that ASCG optimization outperforms standard optimization techniques regarding convergence time and model performance. The proposed ASCG algorithm provides a viable method for improving the training process of backpropagation neural networks, making them more successful in tackling complicated problems across several domains. As a result, the information for initial seeds formed while the model is being trained grows. The coordinated efforts of ASCG’s Conjugate Gradient and ASCG components improve learning and achieve global minima. Our results indicate that our ASCG algorithm achieves 21 percent higher accuracy on the HMT dataset and performs better than existing methods on other datasets(DIR-Lab dataset). The experimentation revealed that the conjugate gradient has an efficiency of 95 percent when utilizing the principal component analysis features, compared to 94 percent when using the correlation heatmap features selection approach with MSE of 0.0678.

Adaptive Stochastic Conjugate Gradient Optimization for Backpropagation Neural Networks

Recently, the Internet of Things and building information modeling (IoT-BIM) has captured attention because of its wide range of applications in multiple domains communicating across different layers. The IoT-BIM consists of three layers, namely physical, network, and application layer. This paper provides and discusses security attacks countermeasure for each IoT-BIM layer. In this paper, we discuss various state-of-the-art IoT-BIM security frameworks and propose a unified security framework for IoT-BIM networks, called “Unified Federated Security Framework”. The proposed framework relies on fuzzy cognitive maps for modeling and evaluating trust relationships between the involved entities in federated identity management systems.

A Conceptual Framework for Securing IoT-BIM

This paper endeavors to create a groundbreaking Hadith authentication mobile system providing Arabic to Malay translation for Android and IOS devices. The background highlights the importance of Hadith in Islamic tradition and the challenges Malay-speaking Muslims face in accessing authentic Hadith resources. The problem statement underscores the language barrier, authenticity concerns, and the lack of comprehensive mobile solutions. The paper’s objectives encompass developing the mobile system, implementing authentication criteria, offering user-friendly features, and enhancing access to authentic Hadith sources. The paper’s significance lies in providing access to authentic Hadith, promoting authentication, and empowering the Malay-speaking Muslim community. The scope includes essential app features and authentication algorithms, while limitations pertain to language support, authentication variability, resource constraints, translation nuances, and continuous updates. This paper aspires to bridge the gap in Hadith accessibility and contribute to a deeper understanding of Islamic tradition among Malay-speaking Muslims. This paper explores the issue of Hadith authentication and scrutiny, focusing on critical scholars’ impressions and the standard used by Hadith compilers. The system uses a client-server architecture and combines web and mobile technologies for administrative and public access. The web interface uses Springboot for Java, while the mobile interface uses React Native for cross-platform applications. The system communicates through rest services and is validated using statistical analysis. The paper also translates authentic Hadith from Arabic to Malay language.

A Novel Hadith Authentication Mobile System for Android and Ios Phones With Arabic to English Language Translation

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