Interoperability in Blockchain: A Survey

Ren, Kai; Ho, Nhut-Minh; Loghin, Dumitrel; Nguyen, Thanh-Toan; Ooi, Beng Chin; Ta, Quang-Trung; Zhu, Feida

doi:10.1109/tkde.2023.3275220

Cited by 24 publications

(2 citation statements)

References 58 publications

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“…So far, several survey papers have been published to encircle the applications of blockchain technologies in various research areas such as cloud computing [4,44], database systems [45], digital twins [46,47], educational technologies [48], e-voting development [49], interoperability [50], smart contracts [51][52][53], internet of things (IoT) [54][55][56][57][58][59][60], and more. In addition, a large number of surveys address issues related to system security, cybersecurity, and privacy of blockchain frameworks [26,[61][62][63][64][65][66][67][68][69].…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cholevas,

Angeli,

Sereti

et al. 2024

Algorithms

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In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cholevas,

Angeli,

Sereti

et al. 2024

Algorithms

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“…An effective combination of CPU and RAM resources is vital for achieving and sustaining high TPS rates in a DLT system, as they contribute to accelerated transaction processing and reduced latency. The formula for calculating TPS can be expressed as Formula (11) [32].…”

Section: F1-score (%)mentioning

confidence: 99%

Lightweight, Trust-Managing, and Privacy-Preserving Collaborative Intrusion Detection for Internet of Things

Wardana,

Kołaczek,

Sukarno

2024

Applied Sciences

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This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

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