Number Of Internet Of Things Devices Research Articles

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

A deep learning-based authentication protocol for IoT-enabled LTE systems

The connected devices in Internet-of-Things (IoT)-enabled systems are continuously increasing nowadays, and likely to grow exponentially worldwide in near future. Hence, the next generation IoT-enabled mobile networks (e.g., 5G onward) are expected to provide higher system capacity and ultra-low latency to deal with. According to the Third Generation Partnership Project (3GPP), Long-Term Evolution (LTE) technology can serve the purpose efficiently, and also bridge the gap between earlier and future generation mobile networks. However, the network may face problems associated with privacy and security, as the underlying communication is mostly wireless. Thus, a secure and efficient Authentication and Key Agreement (AKA) protocol is desirable. Recently, many protocols have been proposed to address these goals. Unfortunately, the security and efficiency of such protocols are still in doubt. This paper introduces a deep learning-based AKA protocol for IoT-enabled LTE systems. The proposed protocol can address mutual authentication among the communicating entities. It employs a Deep Residual Network (DRN)-based key generation technique, called DRN-KeyGen, to establish a shared secret key on-the-fly among the communicating entities in such environment where the number of IoT devices are excessively large and extremely heterogeneous in nature. The security of DRN-KeyGen is verified considering various active and passive attacks that may occur in wireless-enabled LTE systems. The efficiency of DRN-KeyGen is measured through two different parameters: attack detection rate and attack detection time, where each attack is experimented using Python tool-based simulation with varied key lengths. The empirical results show that the proposed DRN-KeyGen can achieve an average detection rate of 0.924 and an average detection time of 30.634 s considering key lengths of 32 bits, 64 bits, 128 bits, 192 bits, 256 bits, and 512 bits. Finally, we have compared the security and efficiency of DRN-KeyGen with other existing protocols to show its superiority.

Model-based, fully simulated, system-level power consumption estimation of IoT devices

Internet of things (IoT) gaining more importance due to its crucial role in pervasive computing and also Industry 4.0. Since the number of IoT devices is scaling up to multiple dozens of billions, the importance of energy efficiency is significantly increased. With the consideration of huge variety of IoT device hardware and software, a comprehensive model and estimation methodology on energy consumption is necessary as an enabler. IoT devices are also frequently updated, upgraded and maintained because of the evolving nature of the requirements and market demands. Each and every such operation has an effect on the power consumption and arose the necessity for a new energy consumption modeling and estimation. This process is applicable for development of IoT devices, as well as the maintenance phase. Since the variety of designs is unlimited, and battery capacity is usually fixed, or a cost factor, a generic, fully simulated, model-based energy consumption estimation of IoT devices is crucial. In this study, we aim to address this problem via proposing fully simulated, model-based, system-level power estimation approaches, as well as their success rate in typical real-life scenarios. It can be seen that the proposed methodology has high accuracy over %97. For the realization of the best-proposed approach, we used Open Virtual Platform (OVP) as an instruction set accurate simulator.

Multiple Access Design for Symbiotic Radios: Facilitating Massive IoT Connections With Cellular Networks

Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

A Review of IoT Security: Machine Learning and Deep Learning Perspective

The Internet of Things (IoT) gives the ability to people and machines to interact and learn from billions of items, including sensors, actuators, services, and other things that are connected to the Internet. The services provided by IoT are becoming an indispensable part of human life, further, the growth in the number of IoT devices is predicted to increase exponentially in the next few years. However, the IoT network consists of a variety of different types of devices being placed in heterogeneous environments. The data these devices generate contains noise in significant proportion. Also, these devices are vulnerable to various different kinds of attacks, identification of which is a challenging task in itself as they are in large numbers. The detection of faults is necessary for the early stages. Generic methods of authentication and authorization are not sufficient for these devices. Further, we need methods that can detect the attack at an early stage based on previous patterns and alert the system at the right time. In recent years machine learning and deep learning (ML/DL) algorithms have advanced considerably from being an experimental project in the research community to being applied in industry-standard applications. These algorithms have the potential to be better than simple security methods in providing intelligence to the system. In this article, we have reviewed various ML/DL algorithms with their advantages, and disadvantages, and how they can be applied in securing IoTs. Initially, a detailed overview of IoT, components, and applications are presented. After that, we present an overview of various threats, and attack methods are discussed with the probable ML/DL solutions.

Privacy in Smart Homes with Remote User Authenticated Key Establishment Protocol

The Internet of Things (IoT) is a set of devices that contain electronic software, sensors, and actuators and exchange information using the Internet or network. Because of multiple applications, the number of IoT devices and sensors is increasing daily. Therefore, the traditional addressing security protocols cannot guarantee performance in all scenarios. The most crucial factor is the requirement of a unique addressing and secure scheme that addresses each smart IoT device or application and ensures secure communication. Therefore, robust and lightweight authentication protocols are crucial to ensuring security in smart home IoT (SH-IoT) networks. The proposed scheme provides a remote authentication protocol for the secure exchange of information between the user and the smart home IoT network. Informal and formal security analyses using the ROR model are presented in this work. The presented scheme is validated using the AVISPA tool.

Classification of cyber threats for internet of things

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

A Multi-Perspective Feature Approach to Few-Shot Classification of IoT Traffic

The realization of Internet of Things (IoT) traffic classification is crucial to the management and monitoring of the IoT network. Performing IoT traffic classification under a few-shot scenario is vital due to extremely rapid growth in the number of IoT devices and necessity to save computational costs. In this paper, we propose MAFFIT, a multi-perspective feature approach to few-shot classification of IoT traffic. The purpose is to comprehensively consider the information of network traffic and to achieve accurate classification of IoT traffic using a limited number of samples. MAFFIT is based on our key observation that traffic behaviour and traffic composition are highly consistent across IoT traffic of the same class. For a flow, MAFFIT will first extract the packet length sequences and packet byte sequence, then encodes the features of the corresponding sequences using feature construction, and finally uses comparative learning to obtain the class of the flow without the additional cost of training a comparison model. We conduct extensive experiments on two real-world IoT traffic datasets, the results demonstrate that MAFFIT can achieve accurate IoT traffic classification using a limited number of flow samples and MAFFIT outperforms three existing network traffic classification methods.

Latency Guarantee for Task Computation in Wireless-Powered Cloud Radio Access Networks

With the massive and rapid deployment of Internet of Things (IoT) devices, the number of IoT devices in the cloud radio access network (C-RAN) has increased dramatically. It is intractable to provide deterministic quality of service (QoS) guarantee for task processing due to the task burstiness and the time-varying channels. In order to guarantee latency requirements of energy-limited IoT devices in C-RAN, this paper proposes a statistical latency guarantee scheme for task computation. With the assistance of densely distributed remote radio heads (RRHs) providing wireless power transfer to the IoT devices, a wireless powered edge computing C-RAN model is constructed. Then, the problem of minimizing task latency violation probability is formulated. With the help of effective capacity theory, the problem is decoupled into multiple sub-problems, which are QoS parameter optimization, task offloading optimization, wireless power transfer and energy allocation. Thereafter, low-complexity schemes are designed to jointly optimize the wireless power transfer, energy allocation and task offloading. The effectiveness of the proposed statistical latency guarantee scheme is finally validated by extensive simulations.

HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, called <i>high-compression federated learning (HCFL)</i>, for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.

IoT Intrusion Detection System Based on Machine Learning

With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.

Energy–Aware security protocol for IoT devices

Internet of Things (IoT) has permeated various aspects of modern life, from smart homes to factories and even gardens. In the coming years, number of IoT devices is expected to surpass that of computers, laptops, mobile phones, and tablets. However, many of these devices are small and operate on batteries, making energy efficiency a significant challenge. This challenge affects all aspects of IoT, including security. To address this issue, we present an adaptive security approach in this paper. Adaptive security involves adjusting the security level based on the level of threats and data context, rather than always assuming the worst-case scenario. This approach reduces energy consumption and is implemented in three parts: 1) Adapting the length of RSA public and private keys, where longer keys provide more security but consume more power. 2) Adapting the trust level between nodes based on the history of the transmitting node, where the receiving node decides whether to verify the correctness of the received messages or not. 3) Utilizing TrustChain, which is transactional verification method inspired by the blockchain concept.We evaluated the performance of our proposed model through exhaustive simulation scenarios and experiments. Our approach outperforms state-of-the-art methods, with the variable key length approach reducing energy consumption by 50%, the trust level approach reducing energy consumption by approximately 50%, and the TrustChain approach reducing energy consumption to 0.771 J, while the blockchain-based method consumed 2.955 J to verify transactions.

Exploring the IoT Data Analytics Landscape: An Extensive Study

Abstract: Since the past decade, the data production and consumption has been increasing exponentially. Ever since the emergence of cheap computer chips, wireless networks, and technological advancements in the cloud, there has been a tremendous increase in the number of IoT devices being developed. The data collected by connected heterogeneous IoT devices are highly diverse and unstructured and need to be analyzed to gain valuable insights therefrom. Data analysis can effectively help Internet of Things (IoT) businesses to identify trends, enhance growth and productivity, and empower employees. To date, immense opportunities are being discovered by analyzing massive amounts of data generated by IoT devices. Through an indepth analysis of existing surveys, we explore IoT, data analytics, and their unification. In this survey, we investigated various applications with their extensive usage and significant challenges of IoT data analytics. By examining the plethora of advantages of data analysis in the IoT sector, one can conclude that field has numerous applications and the true potential of data analysis in IoT sector is yet to be realized.

On the Performance of End-to-End Cooperative NOMA-Based IoT Networks With Wireless Energy Harvesting

This paper studies the end-to-end uplink (UL) and downlink (DL)-outage probability (OP) of an Internet of Things (IoT) network with radio frequency (RF) energy harvesting (EH) over Nakagami-m fading channels. Power-domain nonorthogonal multiple access (NOMA) is adopted to support both the UL and DL transmissions to increase the network spectral efficiency. The system end-to-end UL and DL outage probabilities are analyzed where exact closed-form expressions are derived. The system performance is explored for various system parameters such as time allocation for EH, transmission power, fading conditions, number of IoT devices (IoDs), and data rate. The obtained analytical results are corroborated using Monte Carlo simulation for various operating scenarios. The obtained results show that the optimum harvesting time may broadly vary based on the adopted system parameters. Moreover, the results show that the system OP is highly sensitive to the harvesting time where OP may vary drastically if the harvesting time deviates from the optimum. The impact of the perfect successive interference cancellation (SIC) (PSIC) is also evaluated and compared with imperfect SIC (ISIC), and the obtained results show that the OP of the system can be significantly underestimated with the PSIC assumption. Therefore, the commonly used PSIC assumption may cause substantial deviation from the practical case where the detection process experiences ISIC.

Joint optimization of altitude and resource allocation for aerial cooperative short packet communication system

Unmanned aerial vehicle (UAV) is considered as an important component of 6G Internet of Things (IoT) because of its deployment flexibility. In this paper, we consider a aerial cooperative short packet communication system model in urban environments. The number of transmitted bits is maximized by jointly optimizing the UAV’s altitude, transmit power and blocklength allocation with constraints on system reliability, latency and fairness. The successive convex approximation (SCA) method is adopted to solve the difficulty that the optimization problem is non-convex. Specifically, we firstly decompose the optimization problem into three subproblems to be solved separately, and then obtain the solution of the original problem by the proposed optimization algorithm. Finally, the performance of the proposed algorithm is verified by simulation results. In addition, we investigate the impact of different communication environments and the number of IoT devices on the system performance.

CMTSNN: A Deep Learning Model for Multiclassification of Abnormal and Encrypted Traffic of Internet of Things

With the increasing types and number of IoT devices and malicious programs and the popularization of encryption technology in the communication process between the internet and the Internet of Things (IoT), a large amount of encrypted abnormal traffic among devices endangers IoT cybersecurity. How to identify abnormal encrypted traffic of the IoT has become the premise of cybersecurity. Presently, most of the detection methods for traffic in the IoT have problems such as simple dataset processing, imperfect feature extraction, data imbalance, and low multi-classification accuracy. In this paper, we propose a multi-classification deep learning model named the cost matrix time-space neural network (CMTSNN) for abnormal and encrypted IoT traffic. The CMTSNN is divided into three parts. The first part is the preprocessing stage of the dataset, which needs to retain the timing relation between two data packets in the stream and create a cost penalty matrix according to the sample distribution. Aimed at the robustness of feature extraction in network flow, the second part extracts time series features and then space features to ensure the robustness of feature extraction. The third part is aimed at the problem of data imbalance. The cost penalty matrix is applied to the cost penalty layer in the training process, and then the improved cross-entropy loss function is used to calculate the loss to improve the classification accuracy of minority categories and increase the overall multi-classification performance of the model. Experiments were carried out with the ToN-IoT, BoT-IoT and ISCX VPN-NonVPN datasets. Compared with current methods, the proposed method shows better performances, including accuracy, precision, recall, F1 Score and false alarm rate.

Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model

The fourth industrial revolution is marked by the rapid growth of Internet of Things (IoT) technology, leading to an increase in the number of IoT devices. Unfortunately, this also makes these devices more susceptible to cyber threats, especially DoS/DDoS attacks. While supervised learning models have been adopted to detect and mitigate these threats, they have limitations in detecting unknown attacks that can cause severe consequences. This research aims to address those limitations and provide better protection for IoT networks against DoS/DDoS attacks. We propose a new approach that combines a soft-ordering convolutional neural network (SOCNN) model with local outlier factor (LOF) and isolation-based anomaly detection using nearest-neighbor ensembles (iNNE) models that use both supervised and unsupervised learning methods. We evaluated our approach on three benchmark datasets with varying unknown attack scenarios, and our hybrid model achieved high accuracy in detecting unknown attacks with an average F1-score of 98.94%, 91.68%, and 96.07%, respectively, on BoT-IoT, CIC-IDS-2017, and CIC-IDS-2018 datasets, outperforming state-of-the-art competitors. Our model also showed resilience against adversarial attacks such as the fast gradient sign method (FGSM) and Carlini Wagner (CW) adversarial attacks, highlighting the potential of our approach to enhance IoT network security against DoS/DDoS attacks in unknown attack scenarios.

A lightweight authentication and management method for Internet of Things

Internet of Things (IoT) devices are vulnerable due to their limited resources and inadequate security mechanisms. The number of IoT devices has increased day by day, so the number of devices that are connected to the Internet has also increased. Devices may be deployed anywhere and made available to anyone, making the management of numerous devices problematic. Device management is crucial, as the compromise of IoT devices might cause serious consequences and already there are examples of such a compromise caused widespread DDoS attacks. This paper aims to propose a mechanism that ensures the security of IoT devices and presents a management model and lightweight authentication mechanism simultaneously. The proposed mechanism is novel, computationally lightweight, financially low-cost, remotely usable, and requires no special hardware. This mechanism includes a key management phase to generate ephemeral keys for every session and a trivial and cost-efficient data transfer phase. The proposed mechanism provides an IoT device management model to achieve a scalable IoT environment. Data is transferred cost-effectively via XORs and hash functions. The security of the authentication mechanism is formally verified using AVISPA model checker.

Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks

AbstractThis paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data.