A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking

Kim, Jinoh; Nakashima, Makiya; Fan, Wenjun; Wuthier, Simeon; Zhou, Xiaobo; Kim, Ikkyun; Chang, Sang‐Yoon

doi:10.1109/tnsm.2022.3173598

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…Most of the research studies analysis anomalies in ledger applications, cryptocurrencies, and specific types of attacks like DoS, Sybil or Double Spending etc. A machine learning‐based security technique using Blockchain network traffic statistics is attempted in Reference 97. The presented method consists of two phases, data collection, and anomaly detection.…”

Section: Resultsmentioning

confidence: 99%

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

Chithanuru

Ramaiah

2023

Concurrency and Computation

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SummaryRecently, Blockchain cryptographic distributed transaction ledger technology finds its usage in many applications. The application's ledgers implemented through Blockchain, ensures tamper‐proof transactions, and in turn the applications became robust enough against cyber‐attack But still adversaries put forward their efforts in detecting the vulnerabilities in the infrastructure to execute their ill intent. In the literature, many counter measures techniques are presented to address the security breaches on the Blockchain. Detecting as well mitigating from the possible anomalies against on blockchain infrastructure through AI techniques is the greatest attempt of this article, and which is much needed now. Hence, this review article enlightens the readers with the essence of cyber security, the security aspects of Blockchain, its infrastructure vulnerabilities, various Blockchain‐enabled use cases along with the their challenges. Primarily, anomaly detection on Blockchain infrastructure through Artificial Intelligence Techniques is focused. A detailed analysis of Artificial Intelligence Techniques in detecting the anomalies with the help of Blockchain and also how these two technologies complement each other was demonstrated with the help of suitable use cases. The merits, challenges along with the possible future directions, while integrating Blockchain with Artificial Intelligence Techniques are presented for the benefit of research community.

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Section: Resultsmentioning

confidence: 99%

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

Chithanuru

Ramaiah

2023

Concurrency and Computation

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“… Kim et al. 179 NN-DTW A grid of 1800 trade prices is used to calculate intraday realized volatility over a 30min trade by trade moving window. The NN DTW model correctly identifies 90% of suspected illegal transactions in the validation sample and achieves a type I error rate of 38% on average.…”

Section: Overview Of Cryptocurrencymentioning

confidence: 99%

“…Kim et al. 179 proposed a security mechanism based on analyzing blockchain network traffic statistics to detect malicious events through data collection and anomaly detection functions.…”

Section: Deep Learning In Cryptocurrencymentioning

confidence: 99%

A survey of deep learning applications in cryptocurrency

Zhang,

Cai,

Wen

2024

iScience

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“…The L-space approach refers to the construction of an L-space network, which is based on an actual geospatial network with major road intersections as nodes and traffic lines as edges [14][15]. In contrast to the L-space approach, the P-space approach considers traffic lines as nodes of the network and urban areas as edges.…”

Section: Line Network Structure Constructionmentioning

confidence: 99%

Bilevel Programming Model of Discrete Traffic Network Based on Machine Learning Optimization Hybrid Algorithm

2022

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With the rise of living standards, more and more people choose to travel by car, which increases the pressure on the urban traffic network (TN). The TN needs to be re planned and designed. The main goal of the design, organization and management of the TN is to improve the service level and access to the TN. During the planning process, a reasonable and scientific TN model should be developed according to the characteristics of traffic operation in different regions. Therefore, this paper studies and designs the bilevel programming model of discrete TN based on the hybrid algorithm of machine learning and optimization. This paper first describes the construction of the bilevel programming model and the network structure, then designs the hybrid algorithm and sets the weight coefficient, and finally analyzes the sensitivity of the comparison of the average load degree of the road section and the time budget.

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A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking

Cited by 21 publications

References 38 publications

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

A survey of deep learning applications in cryptocurrency

Bilevel Programming Model of Discrete Traffic Network Based on Machine Learning Optimization Hybrid Algorithm

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