Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar, Tehseen; Talpur, Dhani Bux; Shloul, Tamara al; Ghadi, Yazeed Yasin; Haq, Inayatul; Ullah, Inam; Ouahada, Khmaies; Hamam, Habib

doi:10.3390/brainsci13040683

Cited by 39 publications

(5 citation statements)

References 137 publications

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“…Due to ongoing technological evolution, more complex attacks appear. However, since the IoD and blockchain incorporate additional technologies, the implementation of AI-based mechanisms mitigates this challenge by using neural networks, Deep Learning, and Machine Learning algorithms to optimize the security and privacy of the IoD network [243]. III.…”

Section: Architecture and Deploymentmentioning

confidence: 99%

Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies

Tychola,

Voulgaridis,

Lagkas

2024

Drones

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The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined with IoD principles, offer numerous strengths, e.g., high mobility, wireless coverage areas, and the ability to reach inaccessible locations, including significant improvements such as reliability, connectivity, throughput, and decreased delay. Additionally, emerging blockchain solutions integrated within the concept of the IoD enable effective outcomes that surpass traditional security approaches, while enabling decentralized features for smart human-centered applications. Nevertheless, the combination of the IoD and blockchain faces many challenges with emerging open issues that require further investigation. In this work, we thoroughly survey the technological concept of the IoD and fundamental aspects of blockchain, while investigating its contribution to current IoD practices, the impact of novel enabling technologies, and their active role in the combination of the corresponding synergy. Moreover, we promote the combination of the two technologies by researching their collaborative functionality through different use cases and application fields that implement decentralized IoD solutions and highlighting their indicative benefits, while discussing important challenges and future directions on open issues.

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Section: Architecture and Deploymentmentioning

confidence: 99%

Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies

Tychola,

Voulgaridis,

Lagkas

2024

Drones

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“…Tehseen et al [40] reviewed IoT security issues, threats, and cyberattacks and presented an innovative method to protect all IoT devices by using ML and DL techniques. Moreover, this paper examined how ML and DL can be used to enhance attack detection and mitigation.…”

Section: Related Work On Icn-based Iot With Machine Learning Technolo...mentioning

confidence: 99%

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.

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“…The highlights of the paper (Mazhar et al, 2023) and the transformative impact of the IoT on connectivity and productivity while acknowledging the escalating cybersecurity threats is a good insight. It emphasizes the inadequacy of traditional security measures and advocates for AI-driven solutions, particularly machine learning and deep learning algorithms, to bolster IoT security.…”

Section: Security Concernsmentioning

confidence: 99%

A Structural Equation Model Study for Adoption of Internet of Things for the Growth of Manufacturing Industries in Australia

Mitra,

Seetharaman,

Maddulety

2024

JCBAR

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This research study delves into the integration of IoT within the Australian manufacturing sector, analyzing five key factors influencing adoption and drawing comparisons with Rogers' innovation theory. Its objectives encompass assessing how competitiveness, regulatory support, management support, manufacturing growth, and cost savings affect IoT adoption. Methodologically, the research journey includes defining the problem, conducting a thorough literature review, crafting a research proposal, executing pilot and main studies, validating findings, and ultimately composing the final thesis. Quantitative data obtained from a large pool of over 500 respondents shed light on the positive impacts of competitiveness, regulatory support, and cost savings on IoT adoption. Additionally, the study employs Importance-Performance Map Analysis (IPMA) to underscore the significance of management support in successful IoT implementation. Despite facing limitations such as common method bias and constraints on generalizability, the research underscores the transformative potential of IoT in enhancing operational efficiency and decision-making within Australian manufacturing. Overall, the study's findings confirm the significant influence of key factors on IoT adoption, affirming its potential to drive competitiveness, regulatory compliance, cost efficiency, and growth within the Australian manufacturing landscape.

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Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Cited by 39 publications

References 137 publications

Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies

Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

A Structural Equation Model Study for Adoption of Internet of Things for the Growth of Manufacturing Industries in Australia

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