Chapter 3 - Integrated machine learning techniques for preserving privacy in Internet of Things (IoT) systems

An Internet of Things (IoT)-based system is smart system that provides automated and intelligent services. However, its intelligence still depends on the pre-ruled functions, which are only actuated according to rules without any predictions. To improve the intelligence of IoT and to train it to predict the future, it needs to be integrated with Artificial Intelligence (AI) technologies - either Machine Learning (ML) or Deep Learning (DL). Applying AI techniques in IoT involve some challenges and difficulties. In this paper, we go through some of challenges related to both systems of applying ML in IoT, as well as the issues that resulted from applying some of ML techniques in IoT. Moreover, this paper surveyed the background of IoT and ML and their components, and the usage of ML in IoT with possible applications.

The term Internet of Things (IoT) refers to a technological revolution that allows nearly all environmental devices to connect to the internet and share data to create services and applications that improve our standard of living. As IoT systems generate huge amounts of data, new IoT applications and facilities arise. Machine learning has made significant progress in several fields and is now being used to solve a variety of IoT network problems. Machine learning has recently become more popular as a means of improving IoT applications. Users can find deep analytics and develop efficient intelligent IoT applications using machine learning for IoT. This chapter focuses on offering an overview of machine learning techniques in the IoT platform. The chapter begins with an overview of the IoT, including its history, architecture, and its applications. Then, many machine learning algorithms, various types, and their applications, with examples, are discussed.

Secure message forwarding and privacy in the large-scale heterogeneous Internet of Things (IoT)objects is a challenging task. User data confidentiality with an optimum number of forwarding nodes and minimum message replication rate are the significant factors in IoT networks. In this paper, we present Delay Aware Secure Hashing for Opportunistic (DASHOP) message forwarding in IoT network. Base stations as trusted authority coordinate with mobile social nodes and relay nodes. The divide-and-conquer rule establishes the secure, opportunistic and reliable paths in the network. The principle of optimality reduces the routing cost in IoT network. In the proposed DASHOP scheme, the message replication coefficient and message forwarding cost reduce the degree of channel access request, message transmission rate and jamming effects of adversary nodes in the network. Simulation results indicate that the proposed DASHOP scheme achieves high delivery rate with optimum trust levels and low message forwarding cost as compared to relay-based message forwarding and opportunistic forwarding schemes.

This paper examines the enhancement of security measures for the Internet of Things (IoT) systems through the application of Machine Learning (ML) techniques. As the number of IoT devices continues to rise, ensuring their security has become increasingly critical, given that conventional methods frequently struggle to identify advanced threats. This study explores the implementation of several ML algorithms, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to identify anomalies and intrusions within IoT networks. By conducting a comprehensive review of existing research and experiments, it highlights the effectiveness of ML in enhancing IoT security, with high detection rates for various threats, including botnet attacks, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) incidents, and intrusion attempts. DoS/DDoS attacks and many types of botnets are the most devastating attacks that have been spreading for a long time, and they are still branching out in new ways against IoT networks. They can damage IoT services and prevent these services from being used by legitimate users. Therefore, securing IoT networks becomes a significant concern. The proposed model is used to increasingly monitor network traffic for any deviations from standard patterns IoT networks. This paper also stresses the necessity of utilising suitable datasets and feature selection techniques to enhance the efficacy of ML models. To train our model, we have utilized a dataset called the IoT23 dataset, which is one of the most recent datasets that has many IoT scenarios and anomalous activities. Further-more, we utilised two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), and then we compared the results of these algorithms when training our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by CFS However, for training and testing times metrics, DT performance was superior across both feature selection methods.

The modern developments in the Internet of Things (IoT) have been producing an increasing number of interconnected devices, allowing a variety of elegant applications. These IoT devices generate huge information that require smart data study and processing methods, for instance, deep learning (DL). Remarkably, the DL algorithms, when amalgamated with the industrial IoT (IIoT), can facilitate a mixture of applications, for example, elegant assembling, effective manufacturing, efficient networking, accident identifying, and prevention of errors. Also, IIoT can change the way in which industries use to produce autonomous self-healing machines and improve stocks using machine learning (ML). Alternatively, ML carries hidden imminence in IoT data for quick, automated answers, and enhanced assessment making. ML for IoT can be used to venture future trends, become aware of anomalies, and augment intelligence by consuming images, videos, and audios. Various industries currently use ML in IoT to keep up acceptable standards as well as maintain predictable things in terms of benefits and profits at different times. Banks and other businesses in the financial industry make use of ML technology for two main reasons: to recognise significant imminence in data and to avoid scams. Data mining can also be used to make out clients with high-risk outlines or use cyber-surveillance to locate notifying indications of fraud. IIoT has been a means to digital transformation in manufacturing. It takes up a network of sensors to implement a serious building of data and brings cloud software into action to use this data in expensive approaches about the effectiveness of manufacturing procedures. Machines can gain knowledge of the data and algorithms that are accountable for origin faults in the system to identify the errors before those can do damage. Manufacturers can make use of ML to get better safeguarding processes and facilitate them to construct real-time, smart judgments derived from data. So the main aim of this chapter is to focus on data acquisition, integration, and predictive control in the industry using ML for IIoTs for real-time collection and analysis of thousands of measurement points, an early detection of equipment failures and process disturbances, detection of new types of anomalies as they occur for the first time, and Web-based UI for handling alerts.

The convergence of the Internet of Things (IoT), fog computing, and block chain technology has revolutionized contemporary digital ecosystems by making data management decentralized, efficient, and scalable. Nonetheless, this integration poses serious security and privacy threats. IoT devices produce enormous amounts of sensitive data, making them easy targets for cyber attacks, unauthorized access, and data breaches. Classic cloud-based solutions tend to be challenged by latency, bandwidth limitations, and centralized attacks, making the use of fog computing inevitable to decentralize data processing for better responsiveness and decreased dependency on remote cloud servers. While fog computing offers advantages, it also poses new security threats through possible insider attacks and data integrity issues. Block chain technology solves these problems by presenting a tamper-resistant, decentralized ledger that improves trust, security, and transparency for IoT networks. Block chain, via cryptographic authentication, consensus protocols, and smart contracts, ensures the secure transaction of data while alleviating risks against unauthorized data manipulation. Scalability, computational complexity, and energy efficiency are still challenges that need to be met for widespread IoT adoption. This chapter discusses the complex security and privacy problems in IoT, fog, and blockchain networks and examines current threats and countermeasures. Advanced cryptographic methods, secure communication schemes, and access control techniques for strengthening the security of these connected systems are mentioned. It further presents future research directions and trends that will improve a safer, more efficient, and privacy-augmented digital infrastructure.

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.

This paper presents HC-LBR routing, the first location-based routing (LBR) mechanism that simultaneously retains high communication efficiency and privacy for Internet of Things (IoT). Existing LBR schemes were originally designed for wireless sensor networks (WSNs). Although they offer attractive efficiency and scalability, their privacy performance presents a vulnerability when being used in IoT. This is because, unlike a conventional WSN where all nodes are owned by the same user, an IoT network has an open architecture in which nodes owned by different users are mixed and work together. Therefore, when LBR is directly used in an IoT network, the sharing of location information among alien nodes allows one user to peek into the communication privacy of other users. HC-LBR overcomes this privacy weakness by computing efficient geographic routes directly based on Hilbert-Curve-encrypted location information. HC-LBR consists of two components: A Kademlia-tree-based routing algorithm that supports efficient geographic routing in HC encrypted space, and Rand-Mix, a lightweight traffic mixer that uses multiple traffic flows to enhance the privacy of HC routing. Our simulation-based evaluation verifies the high efficiency and privacy of the proposed methods.

With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.

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.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

Artificial intelligence (AI) is the ability of any mechanical device to imitate human intelligence processes. Machine learning (ML) is a technology subset of AI that searches through data, understands them, predicts the pattern, and independently builds an algorithm, thus allowing the computers to become independent and program automatically. The “Internet of things (IoT)” is a revolutionary technology that embeds the Internet into physical devices, thus granting them capabilities of identification and utilization of whatever form of input/output they are designed to give or take. It also opens the window to cross globe connection. IoT, being an incredibly huge network of connected devices that allow data to be easily shared and retrieved surpassing the boundaries of time, region, and space, gives rise to the concept of ubiquitous or pervasive computing, which integrates the features of the Internet, digital, and communication sectors into one technology, thereby enabling the machines to become truly independent. This work is a comprehensive survey on the IoT paradigm highlighting its features and applications. It introduces the reader to basic concepts of AI and machine learning and then goes on to describe IoT using various examples, leading to the delivery of a complete picture of the world of AI-based IoT as of now.

Internet of Things (IoT) systems are rightly receiving considerable interest for many real-world applications, from in-body networks to satellite networks. Such a massive-scale system generates a considerable amount of traffic data, making IoT systems a distributed data source generator. For many reasons, such as the functionality of IoT applications and Quality of Service (QoS) provisioning, classifying these traffic data is of high importance. In the last few years, widespread interest has been expressed in applying Machine Learning (ML)-based techniques for Network Traffic Classification (NTC) tasks. However, the traditional centralized learning-based traffic classifiers pose serious challenges, especially in IoT networks. The centralized ML techniques call for collecting a large amount of data from various IoT devices, which in turn introduces data governance and privacy challenges. Furthermore, in the centralized ML, training data need to be transferred to the Cloud, which increases communication cost and latency. To address these problems, we propose Federated Learning (FL) Internet of Things (IoT) Traffic Classifier (FLITC)-a Federated Learning (FL)-based IoT traffic classification method which is based on the Multi-Layer Perception (MLP) neural network and holds the local data unimpaired on IoT devices by sending only the learned parameters to the aggregation server. Our experimental results show that the FLITC beats centralized learning in preserving the privacy of sensitive data and offers a better degree of accuracy at the cost of a longer training time.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

The Internet of Things (IoT) has transformed various sectors by enabling connectivity and automation through smart devices. However, the expansion of IoT networks has introduced new security challenges, particularly concerning the detection and prevention of cyber threats. This study explores the application of machine learning (ML) models for enhancing network security in IoT-enabled smart systems. The primary objective was to evaluate the performance of different ML algorithms, including Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Means, and Autoencoder, in detecting and mitigating security threats in IoT environments. The study also aimed to identify the most effective model in terms of accuracy, precision, recall, and F1-score. The research methodology involved training and testing these models on publicly available IoT security datasets, followed by performance evaluation based on the key metrics. Statistical analysis, including One-Way ANOVA and post-hoc testing, was used to assess the significance of the performance differences across the models. The results revealed that CNN outperformed all other models, achieving the highest accuracy (96.7%), precision (95.8%), recall (97.2%), and F1-score (96.5%). Random Forest also demonstrated strong performance with an accuracy of 95.6% and a balanced evaluation across all metrics. In contrast, traditional models like SVM and DT showed comparatively lower performance, while Autoencoder struggled with classification tasks, particularly in real-time threat detection. Statistical analysis confirmed the significant differences in model performance. In conclusion, the study demonstrates the effectiveness of deep learning and ensemble models, particularly CNN and Random Forest, in enhancing IoT network security. The research highlights the importance of selecting the appropriate ML model based on the specific requirements of the IoT environment and computational constraints, and suggests that hybrid models and model optimization strategies may offer promising solutions for resource-constrained IoT systems.