Deep Learning For Anomaly Detection In A Blockchain- Secured Opioid Supply Chain

Abstract

The Opioid Crisis Is A Significant Public Health Challenge Exacerbated By Vulnerabilities In The Conventional Supply Chain, Including Diversion, Counterfeit Drugs, And Over- Prescription. This Paper Proposes A Novel System That Leverages Blockchain Technology To Create A Secure, Immutable, And Transparent Ledger For Tracking Opioid Distribution. By Integrating This Secure Data Source With A Deep Learning Model, We Demonstrate A Highly Effective Method For Identifying And Classifying Anomalous Transactions. Our Analysis Of A Simulated Dataset Reveals That Suspicious Activities Are Strongly Correlated With An Unusually High Quantity Of Drugs. The Deep Learning Model, A Multi-layer Perceptron (MLP), Was Trained On These Data Patterns And Achieved A Flawless Performance With 100% Accuracy, Precision, And Recall On The Test Set, Successfully Distinguishing Between Normal And Suspicious Transactions. The Findings Validate The Potential Of This Integrated Approach To Provide Actionable Insights For Regulatory Bodies And Law Enforcement, Thereby Strengthening The Opioid Supply Chain And Contributing To The Global Effort To Mitigate This Crisis.

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