Smart cities under attack

Staudemeyer, Ralf C.; Voyiatzis, Artemios G.; Moldovan, George; Suppan, Santiago; Lioumpas, Athanasios S.; Calvο, Daniel

doi:10.1201/b22142-11

Cited by 1 publication

(2 citation statements)

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“…In traditional financial systems, credit card frauds are the most common frauds, and AI techniques are used to solve these frauds. As a result, the financial industry suffers a loss of billions of dollars each year due to these frauds [ 1 ]. In [ 31 ], the authors employed unsupervised machine learning techniques to detect the monetary anomalies.…”

Section: Problem Statement and System Modelmentioning

confidence: 99%

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A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Ashfaq

Khalid

Yahaya

et al. 2022

Sensors

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In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

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Section: Problem Statement and System Modelmentioning

confidence: 99%

“…Additionally, as the volume of transactions rises, there is an increase in fraud associated with financial transactions. As a result, billions of dollars are lost globally every year [ 1 ]. Any suspicious activity on a network that behaves abnormally is called an anomaly.…”

Section: Introductionmentioning

confidence: 99%