Demand response scheduling based on blockchain considering the priority of high load energy enterprises

Zhu, Xiaomin; Liu, Yu; Cao, Yang; Zhang, Jiao

doi:10.1016/j.egyr.2023.05.044

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…However, to usher in further advancements, the second era of blockchain has been based on smart contracts and finds the researchers diligently working on integrating the innovative feature of programmability into the network, all thanks to the development of smart contracts [6,7]. The decentralized features of a blockchain network manifest its transparent nature, resulting in secure and effective data storage and data analysis applications, which span a wide range of research areas such as finance [5,8,9], health [10][11][12], IoT [13,14], industry [15], enterprise [16,17], etc.…”

Section: Introductionmentioning

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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