Intrusion Detection System for IoT Heterogeneous Perceptual Network

Zhou, Man; Han, Lei; Lu, Hongwei; Fu, Cai

doi:10.1007/s11036-019-01483-5

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…Equally, to enhance the security and preserve-privacy of IoT data, different techniques have been utilized, such as cryptocurrency and blockchain [30,44,49,52,69,78,84,94,99], cryptography-MGO and DHPEA [78], PUFs(ECDSA) [94], homomorphic encryption [51], routing/two-fish symmetry key [85], authentication [68], access control [36], DP [51,52,82]. Additionally, most strategies incorporate techniques to minimize energy consumption, such as in [38,56,72,93,99], while others employed ByzantineMedia and ByzantineKrum [45] to ensure the reliability of the deployed IDS model. With these approaches, the finding further revealed that while the TL and ensemble techniques aimed to speed up model training and detection accuracy, as well as boost data security, the FL strategies were geared towards improving accuracy, efficiency, data imbalances, reliability as well as preserve security and privacy of the data [37,51,52,82].…”

Section: Discussionmentioning

confidence: 99%

“…This solution proved effective in detecting sinkholes and selective-forwarding attacks, with a 76.19% True Positive Rate TPR and a 5.92% FPR for concurrent wormhole attacks. In addition, Zhou et al [93] addressed security and energy issues in heterogeneous IoT networks by devising a dynamic IDS placement strategy using the ULEACH clustering algorithm. This approach optimizes node threshold computation, enhancing node utilization and network performance while conserving energy.…”

Section: Unsupervised Learning Basedmentioning

confidence: 99%

“…Moreover, other metrics used include IDS model training and testing time, efficiency is used to measure the rate of IDS' FPs and FNs in terms of computational resources and accuracy, the response time [32,54,79], which is expected to be swift since IoT devices often operate in real-time environments, energy utilization [56,63,89,93,99,100] which measure the energy consumed by the IDS as IoT devices are frequently resource-constrained with limited battery life. Moreover, most studies also measure the CPU utilization or computational cost [32,54,62,68,79,100], which requires IDS to have low CPU usage to not interfere with the main functions of the IoT device, memory usage [32,54,79], which requires IDS to be lightweight due to the limited memory resources of IoT devices.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

Isong,

Kgote,

Abu-Mahfouz

2024

Electronics

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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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Section: Discussionmentioning

confidence: 99%

Section: Unsupervised Learning Basedmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

Isong,

Kgote,

Abu-Mahfouz

2024

Electronics

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“…Hence, they cannot be applied directly to most resource-constrained devices such as IoT devices or sensors. Thus, it is a challenging task to develop lightweight, fast, and efficient secure cryptographic mechanisms for the IoT that can verify the identity of the sender and prevent the revelation of sensitive information by unauthorized attackers [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Unforgeable Digital Signature Integrated into Lightweight Encryption Based on Effective ECDH for Cybersecurity Mechanism in Internet of Things

Ahmed

Barukab

2022

Processes

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Cybersecurity protocols enable several levels of protection against cyberattacks (digital attacks) that spread across network devices, platform programs, and network applications. On the Internet of Things (IoT), cyberattacks are generally intended to access and change/destroy sensitive information, which may reduce IoT benefits. Moreover, recent IoT systems are experiencing a critical challenge in designing a lightweight and robust cybersecurity mechanism on resource-constrained IoT devices. The cybersecurity challenges facing the IoT that should be taken into consideration are identifying compromised devices, data/service protection, and identifying impacted IoT users. This paper proposes an unforgeable digital signature integrated into an effective lightweight encryption (ELCD) mechanism that utilizes the secure key distribution in an elliptic curve Diffie–Hellman (ECDH) and resolves the weak bit problem in the shared secret key due to the Diffie–Hellman exchange. The ELCD mechanism proposes a secure combination between the digital signature and encryption, and it uses fast hash functions to confidentially transfer a shared secret key among IoT devices over an insecure communication channel. Furthermore, the ELCD mechanism checks the true identity of the sender with certainty through the proposed digital signature, which works based on a hash function and three steps of curve-point inspection. Furthermore, the security of ELCD was mathematically proven using the random oracle and IoT adversary models. The findings of the emulation results show the effectiveness of ELCD in terms of CPU execution time, storage cost, and power consumption that are less by 53.8%, 33–17%, and 68.7%, respectively, compared to the baseline cryptographic algorithms.

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“…As for the perception layer of the IoT, due to its heterogeneity and complexity. It is very vulnerable to be attacked by criminals, resulting in security threats to the source of the perceptual data [4,5]. Ensuring the credibility of the perception nodes and the source of perception data is the cornerstone of ensuring the safe operation of the perception layer.…”

mentioning

confidence: 99%

A remote attestation mechanism using group signature for the perception layer in centralized networking

Huang

Zhang

Shen

et al. 2022

J Wireless Com Network

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As the important transmission approach of the perception data, the security and privacy of the perception layer nodes have been paid more and more attention. To evaluate the credibility of the perception nodes and protect autologous identity privacy, a remote attestation mechanism using group signature (GS-RAM) for the perception layer of centralized networking is proposed in this paper. First, a RAM, based on the computational Diffie–Hellman (CDH) problem and GS, is established for the data source of the perception nodes. Second, the specific construction of the proposed GS-RAM is given to identify the trusted state of data sources without exposing the privacy of the perception nodes. Then, a strict security certification was carried out to verify the correctness, unforgeability, anonymity, traceability, unrelatedness, non-framing, anti-joint aggression and forward security of the proposed GS-RAM. Finally, simulation experiments are carried out to verify that the proposed scheme has better security and dynamic adaptability.

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Intrusion Detection System for IoT Heterogeneous Perceptual Network

Cited by 11 publications

References 31 publications

Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

Unforgeable Digital Signature Integrated into Lightweight Encryption Based on Effective ECDH for Cybersecurity Mechanism in Internet of Things

A remote attestation mechanism using group signature for the perception layer in centralized networking

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