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Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review

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arxiv 2311.11705 v1 pith:I6G6OJF2 submitted 2023-11-20 cs.CR cs.NI

classification cs.CRcs.NI
keywords securitytrust-basedapproachescybersecurityreviewthreatsattackschallenges
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The continuous rise in the adoption of emerging technologies such as Internet of Things (IoT) by businesses has brought unprecedented opportunities for innovation and growth. However, due to the distinct characteristics of these emerging IoT technologies like real-time data processing, Self-configuration, interoperability, and scalability, they have also introduced some unique cybersecurity challenges, such as malware attacks, advanced persistent threats (APTs), DoS /DDoS (Denial of Service & Distributed Denial of Service attacks) and insider threats. As a result of these challenges, there is an increased need for improved cybersecurity approaches and efficient management solutions to ensure the privacy and security of communication within IoT networks. One proposed security approach is the utilization of trust-based systems and is the focus of this study. This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT. A total of 23 articles were identified that satisfy the review criteria. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats and grouped them into three major categories, namely: Observation-Based, Knowledge-Based & Cluster-Based systems. Finally, several open issues were highlighted, and future research directions presented.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks

    cs.CR 2025-06 conditional novelty 4.0 of 10

    Predictive-CSM combines a behavioral trust score with chained HMAC fragment validation and reports simulated packet delivery above 97 percent under 6LoWPAN fragmentation attacks.

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