Internet Of Things Environment Research Articles

Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the IT community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

Challenges and Future Trends of Cryptography in Internet of Things

The incorporation of cryptographic techniques is crucial for guaranteeing data privacy and security processed additionally sent inside IOT ecosystems, particularly as the IOT keeps growing. Examining problems including resource limitations, scalability, and the dynamic nature of IOT environments, this research paper explores the complex obstacles that cryptographic solutions confront considering the IOT. Lightweight cryptography, post-quantum cryptography, and blockchain integration are some of the new trends and future prospects in cryptographic research that are examined in this study in an effort to address these issues. This work offers a useful viewpoint for scholars, practitioners, and legislators engaged in the constantly changing field of cryptographic protocols inside the IOT framework by clarifying current problems and projecting future advancements. Keywords— Cryptography, Internet of Things (IOT), Security,Challenges

SDACS: Blockchain-Based Secure and Dynamic Access Control Scheme for Internet of Things.

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.

A review on machine learning based intrusion detection system for internet of things enabled environment

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.

An attribute-based access control scheme using blockchain technology for IoT data protection

With the wide application of the Internet of Things (IoT), storing large amounts of IoT data and protecting data privacy has become a meaningful issue. In general, the access control mechanism is used to prevent illegal users from accessing private data. However, traditional data access control schemes face some non-ignorable problems, such as only supporting coarse-grained access control, the risk of centralization, and high trust issues. In this paper, an attribute-based data access control scheme using blockchain technology is proposed. To address these problems, attribute-based encryption (ABE) has become a promising solution for encrypted data access control. Firstly, we utilize blockchain technology to construct a decentralized access control scheme, which can grant data access with transparency and traceability. Furthermore, our scheme also guarantees the privacy of policies and attributes on the blockchain network. Secondly, we optimize an ABE scheme, which makes the size of system parameters smaller and improves the efficiency of algorithms. These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments. Thirdly, to prohibit attribute impersonation and attribute replay attacks, we design a challenge-response mechanism to verify the ownership of attributes. Finally, we evaluate the security and performance of the scheme. And comparisons with other related schemes show the advantages of our proposed scheme. Compared to existing schemes, our scheme has more comprehensive advantages, such as supporting a large universe, full security, expressive policy, and policy hiding.

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy Commitment

The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.

Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

Machine Learning (ML) and better encryption methods is a key part of fixing the security and speed problems that Edge Computing causes in the Internet of Things (IoT) and Wireless Networks. The goal of this study is to improve the security of edge devices and networks so that critical data created and processed at the edge stays private and secure. Anomaly detection, threat identification, and adaptable security mechanisms depend on machine learning algorithms in a big way. These algorithms allow for proactive defenses against cyber dangers that are always changing. The proposed system used homomorphic encryption and quantum-resistant cryptography, to make data more private and secure. Even in edge devices with limited resources, these security methods keep data transfer and storage safe. The combination of machine learning and stronger encryption not only protects the IoT environment but also makes the best use of resources by changing security measures on the fly as threats change. This study adds to the development of safe and effective edge computing models, which helps IoT and wireless networks become more popular. The results can be used in many situations, from smart cities to industrial robotics. This makes sure that the advantages of edge computing can be enjoyed without putting the safety and privacy of the linked systems at risk.

IoT Threat Mitigation: Leveraging Deep Learning for Intrusion Detection

The growth of smart gadgets connected via the Internet of Things (IoT) in today’s modern technology landscape has substantially improved our everyday lives. However, this convenience is juxtaposed with a concomitant surge in cyber threats capable of compromising the integrity of these interconnected systems. Conventional intrusion detection systems (IDS) prove inadequate for IoT due to the unique challenges they present. We propose and evaluate an intrusion detection system (IDS) based on a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in this paper. The model is designed to capture both temporal and spatial patterns in network data, offering a robust solution for detecting malicious activities within IoT environments. The CNN-LSTM model displayed excellent accuracy, reaching 98% in both multi-class and binary classifications when trained on the UNSW-NB15 dataset. Furthermore, we explore the real-world applicability of the model through testing on Raspberry Pi, showcasing its effectiveness in IoT scenarios. The system is augmented with alert mechanisms, promptly notifying relevant parties upon intrusion detection. Our findings highlight the CNN-LSTM model's efficacy in strengthening IoT network security.

GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things.

The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%.

AI-powered biometrics for Internet of Things security: A review and future vision

Biometrics is a set of advanced technologies that use the physical or behavioral characteristics of individuals to provide reliable access control. With the rapid development in information and communication technology, biometrics has become an increasingly important field, especially with the integration of artificial intelligence (AI). Fingerprint- and facial-recognition technologies have been significantly improved by the use of AI. The widespread use of the Internet of Things (IoT) has led to security challenges and created new opportunities for biometrics to be used in various applications. This review provides a comprehensive analysis of intelligent or AI-powered biometrics, focusing on the integration of AI and biometrics for building security measures to improve IoT security. Due to their enhanced security and usability, biometric-based authentication methods are becoming increasingly popular in IoT environments. AI algorithms are essential for improving biometric systems in IoT applications by enabling advanced pattern recognition and adaptive decision-making. Furthermore, the integration of biometrics with AI and the IoT can help mitigate security risks, ensuring the protection of data and user privacy. This review makes three main contributions: it provides a comprehensive analysis of the interdependencies between the AI, biometrics, and IoT domains; it covers the applications of AI and biometrics in the context of the IoT; and it highlights the current challenges and future research directions for the deployment of intelligent biometrics in various IoT application domains. Furthermore, this review facilitates further research in the area of the application of intelligent biometrics in IoT applications.

Advancing Video Data Privacy Preservation in IoT Networks through Video Blockchain

In the digital age, where the Internet of Things (IoT) permeates every facet of our lives, the safeguarding of data privacy, especially video data, emerges as a paramount concern. The ubiquity of IoT devices, capable of capturing and disseminating vast quantities of video data, introduces unprecedented challenges in ensuring the privacy and security of such information. This article explores the crucial intersection of video data privacy and blockchain technology within IoT networks. It aims to uncover and articulate the unique challenges video data encounter in the IoT ecosystem, such as susceptibility to unauthorized access and the difficulty in ensuring data integrity and confidentiality. By conducting a thorough literature review, this study not only illuminates the intricate privacy challenges inherent in IoT environments but also showcases the immutable, decentralized nature of blockchain as a potent solution. We systematically explore how blockchain-based methods can be pragmatically implemented to fortify video data privacy, scrutinizing the efficacy of these approaches in the IoT context. Through critical assessment, the paper delineates the strengths and limitations of video blockchain solutions, underscoring the transformative potential of blockchain technology as a cornerstone for enhancing data privacy in IoT networks. Conclusively, this work advocates for blockchain as an indispensable tool in the advancement of data privacy measures for video content, thereby reinforcing trust and security in the increasingly connected fabric of our digital world. As IoT applications burgeon, the fusion of blockchain technology with IoT infrastructures promises a robust framework for protecting sensitive video data, heralding a future of enhanced trust and security in our interconnected ecosystem.

Analysis of Fruiting Body Diversity of Wild Macrofungi in Helan Mountain under Internet of Things Environment

Analysis of Fruiting Body Diversity of Wild Macrofungi in Helan Mountain under Internet of Things Environment

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection-Current Research Trends.

With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

Distributed Multiband Synthetic Aperture Radar Image Fusion Based on Wavelet Transform in the Internet of Things Environment

Distributed Multiband Synthetic Aperture Radar Image Fusion Based on Wavelet Transform in the Internet of Things Environment

Integrating Intelligent Systems in MANET- IoT Environment based on Subjective Context

 The Internet of Things (IoT) has cultivated a promising environment, pivotal across diverse sectorsvia MANET protocols within the w.w.w. This technology significantly contributes to advanced services for mobiledevices in the MANET-IoT domain. However, MANET encounters security challenges, notably malicious attacksas a prime concern. To counter these threats, preventive measures like subjective-context evaluation and nodeprotection are employed, ensuring data packet integrity before delivery. This study aims to enhance theperformance of a novel reactive protocol, integrated with a fuzzy system within the MANET-IoT environment. Themethodology is implemented in two directions: 1) Establishing a secure network founded on trust parameters andshortest path selection. 2) Incorporating an integrated fuzzy system into an improved reactive routing protocolwithin the MANET environment. Subjective context ensures a secure milieu, evaluating nodes before packetdelivery based on trust and shortest path. The fuzzy system's rules determine the network's probability distribution,contingent upon trust and the number of hop counts. The proposed routing protocol is evaluated both pre- and postattacks during packet transmission. Results demonstrate its superiority in packet transmission over the originalprotocol. Three scenarios evaluate packet delivery fraction (PDF), end-to-end delay (E2E), and normalized routingload (NRL). FS-AODV's percentages were 0.9873% against AODV's 1.9871% without drop) and 0.8419% with adrop for PDF. FS-AODV's percentages stood at 0.0006% against AODV's 0.0007% without a drop and 0.0005%with a drop for E2E. In contrast, FS-AODV's percentage was 0.058%, AODV at 0.0127% without a drop, and0.066% with a drop for NRL.

A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks

This study explores the growing challenges of cybersecurity in the context of rapidly adopted Internet of Things (IoT) technologies, which have become increasingly susceptible to cyber threats. The widespread utilisation of IoT systems intensifies the complex interactions between devices and amplifies the traffic of data, creating various opportunities for cyber adversaries. Consequently, detecting and mitigating cyberattacks targeting IoT systems has emerged as a critical imperative in the field of cybersecurity.The primary objective of this study is to employ various machine learning methods to detect cyber anomalies within IoT systems and subsequently compare the efficacy of these methods. Comparative analysis encompasses various machine learning techniques, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Logistic Regression (LR), and k-Nearest Neighbours (k-NN). This technical evaluation aims to provide a nuanced perspective on the contributions of these methods to the classification of cyber attacks in IoT systems. Performance analysis of these methods serves as valuable information for cybersecurity experts, offering guidance in the development of robust protection strategies for the IoT ecosystem. As IoT security continues to gain prominence, the findings of this study are poised to contribute significantly to the refinement of cybersecurity practices and the fortification of IoT environments against potential threats. In this study, we use different machine learning methods to detect anomalies in cyber attacks on IoT systems and compare the performance of these methods. The result shows that the neural network performed better than the other models.

Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments.

The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.