Enhancing the blockchain voting process in IoT using a novel blockchain Weighted Majority Consensus Algorithm (WMCA)

The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

iPMRSS: An Improved privacy-preserving medical record searching scheme for intelligent diagnosis in IoMT

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target Denial of Service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new committee members auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir's secret-sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB's resistance against double-spend attacks, an improved Nakamoto's attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

The advancements in the field of Information and Communication Technology (ICT) have led to the development of the Internet of Things (IoT), where ordinary things (e.g. smart meters, sensors etc.) can be connected and controlled over the internet. However, an IoT network is vulnerable to many malicious network attacks due to its inherent properties like limited computational abilities, heterogeneity, massive connectivity, etc. This paper presents a feature selection technique, which uses an amalgamation of LASSO regression and correlation based techniques, to find appropriate features for IoT network attack detection. The effectiveness of the proposed mechanism was evaluated on benchmark IoT network datasets using different machine learning techniques. The results revealed that the Gradient Boosting ensemble learning model achieved a maximum attack detection accuracy of 99.98%, and outperformed the other studied models.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

One of the modern trends in the development of information and communication technologies that have evolved into the Internet of Things (IoT) paradigm is the progressive idea of using blockchain technology in IoT. This trend is described in a large number of modern literature sources, which point out the significant advantages of using blockchain, primarily related to decentralization, immutability, transparency and security. This allows us to conclude that the implementation of blockchain technology in IoT information and communication systems is highly relevant. However, the authors of different studies describe significant problems, including the problem of scalability of the blockchain itself, as well as the problems of energy efficiency and limited resources in IoT devices. Due to these problems, the implementation of blockchain technology in the Internet of Things is becoming a challenge and determines the relevance and feasibility of researching this topic and finding effective solutions related to the integration of two very powerful technologies in modern information and communication systems. The article presents an analytical overview of the issues of integrating blockchain technology into IoT, in particular, the use of the Proof-of-Work (PoW) consensus algorithm in IoT and the use of alternative blockchain protocols, including IOTA, Ethereum Light Client and Litecoin, which allow increasing the efficiency of performing tasks in IoT infrastructure, taking into account the limited resources of devices; existing problems and implementation methods, such as the use of light clients and proxy servers. The article also considers the feasibility of using a full blockchain node on IoT devices and ways to increase system efficiency by pre-processing data before sending it to the blockchain network. The article also highlights the problem of insufficient implementation of blockchain technology in various spheres of human life, as well as the search for the reasons for this problem. The results of the analytical review make it possible to substantiate the purpose and advantages of integrating blockchain technology into the Internet of Things, as well as to summarize the main issues and substantiate a number of tasks for relevant and progressive scientific research in the field of modern information and communication systems.

The Internet of Things (IoT) has become a technological innovation that brings many benefits in various sectors, but also presents challenges, especially in terms of cybersecurity. One of the main threats is malware, which can damage devices, steal data, and disrupt system performance. With the increasing use of IoT, malware attacks on IoT devices are a serious concern. Previous research shows that malware detection models in IoT devices still have shortcomings, especially in terms of accuracy. One of the algorithms used in malware detection, Naïve Bayes, has been shown to provide low accuracy results. This study aims to improve the accuracy of malware detection on IoT networks by applying Ensemble learning techniques using traffic data from the IoT-23 dataset. The methodology used refers to the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, which includes the stages of domain understanding, data understanding, data preparation, modelling, evaluation, and deployment. The results show that Ensemble learning improved the performance of individual models. Naïve Bayes as a single model produces an accuracy of 0.24, increasing to 0.35 when combined with AdaBoost, and 0.99 when combined with XGBoost. The combination of the three models also produced an accuracy of 0.99. These results demonstrate the effectiveness of ensemble learning in improving malware detection accuracy in IoT environments.

The authentication procedures are being utilized among the various Network & Communication Systems across the globe, which includes the healthcare networks, wireless sensor networks, Wi-Fi, etc. In the case of IoT networks, the small computational powered nodes are utilized as IoT sensors. The lower computational power creates the major hurdle in the implementation of security algorithms in the IoT node framework. A strong need of robust authentication mechanism with low resource requirement needs be developed to ensure the security of IoT networks. In this paper, the new authentication scheme is proposed over 6-column key table. The complex authentication keys are produced from the selective columns by using the combination of trigonometric functions. This model is created to protect data privacy against the Man-in-the-Middle (MITM) attacks over the IoT networks. The proposed model is designed for the enhancement in the level of security, while optimizing the elapsed time for key table generation (element-key table generation) and key preparation (complex key generation from element-keys). Various experiments have been conducted over the proposed model design and results are obtained in the form of the performance metrics of key table generation and key transmission and verification during the exchange of the keys. The proposed model is found efficient and secure for the purpose of authentication over the healthcare networks based upon the internet of things (IoT).

Internet of Things (IoT) refers to the network of interconnected smart physical objects that collect and exchange information for enabling smart applications and services. IoT devices are often battery powered and lack computational resources and as a consequence, traditional measures of providing security is not feasible in IoT systems which leads to major security vulnerabilities. Blockchain technology has proven to be a secure medium of transaction in trustless public networks and has the potential to resolve the security vulnerabilities intrinsic to IoT systems. However, reaching consensus throughout a blockchain network is computationally demanding and requires large amount of energy. Moreover, typical transactions in blockchain based applications are slow, compounding the issues in adopting blockchain technology in IoT systems. In this work, a lightweight consensus algorithm called Proof-of-Authentication (PoAh), was implemented on resource-constrained IoT edge nodes and evaluated in terms of latency and energy consumption. The implemented consensus algorithm reduces latency in block validation to 29.35 ms and energy consumption to 44.31 mJ for each transaction, demonstrating promise and potential, to feasibly implement blockchain technology in IoT systems.

The Internet of Things (IoT) has emerged as a crucial expansion of Internet services, linking numerous devices that produce and exchange substantial quantities of data. Strong defense against evolving cyberattacks that jeopardize the confidentiality, integrity, and availability (CIA Triad) of IoT systems is necessary due to this interconnectedness. Therefore, by controlling network activity to stop invasions and identify odd data patterns, intrusion detection systems can offer a robust layer of security for IoT systems. With a focus on identifying less frequent Advanced Persistent Threats (APT), this study aims to detect attacks directed at the CIA Triad. We examined two popular and unbalanced data sets, TON-IoT and BoT-IoT, for anomaly detection in IoT networks. By examining the usefulness of the ensemble learning and deep learning models in anomaly detection inside the IoT network data, we were able to study the security element of the network. The ensemble learning models considered are the following. Random Forest, XGBoost, and Stacking. The considered deep learning models are as follows: BiLSTM, GRU, Autoencoder, and Self-Taught Learning (STL: AE-SVM). The performance metrics studied are as follows: Accuracy, False Alert Rate, Recall, Precision, and F1-score. Regrettably, there were instances in which anomalies could not be detected using conventional techniques. This is because labeled data is scarce, particularly for APT attacks, and attack traffic is frequently sparse and noisy. We suggested a deep self-taught learning system (STL: AE-XGB) based on Transfer Learning to detect anomalies in IoT network data to tackle these difficult issues. To enhance classification performance and capture the changing landscape of attack methods in IoT networks. Our models automatically augment annotated data. The results show that our proposed model (STL: AE-XGB) outperforms the conventional models studied and provides interesting results in binary and multiclass classification.