article · Electronics
This research provides a detailed analysis of recent advancements in Intrusion Detection Systems (IDS) for Internet of Things (IoT) ecosystems, covering developments from 2016 to 2023. It examines various detection methodologies, including machine learning and non-machine learning approaches like signature, anomaly, specification, and hybrid models, designed to counter IoT-specific threats. The analysis also explores different deployment models, from edge to cloud computing, and evaluates IDS performance using metrics such as accuracy, false positive rates, and computational costs across various benchmark datasets. The study identifies methods to improve IDS accuracy and efficiency, such as feature engineering, optimisation, and the integration of cryptographic and blockchain technologies. It also highlights key challenges, including resource limitations of IoT devices, scalability, and privacy concerns, while proposing future research directions to enhance IoT security.
As Internet of Things devices become ubiquitous, ensuring their security is paramount. This research is important because it provides a comprehensive overview of how current Intrusion Detection Systems protect these devices, highlighting their strengths and weaknesses. Understanding these aspects is crucial for developing more robust security measures, making IoT ecosystems safer and more trustworthy for users and organisations.
This research provides a foundational analysis that can inform the development of more effective Intrusion Detection Systems for Internet of Things environments. It could guide cybersecurity solution providers and IoT platform developers in designing and implementing enhanced security features. The findings highlight areas for improvement in existing technologies and suggest future research pathways, indicating it is early-stage research that supports the creation of future commercial security products.
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The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.
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DOI: 10.3390/electronics13122370
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