Fortifying Retail Security: Leveraging Business Analytics, Machine Learning, and Blockchain Integration for Enhanced Cyber Protection

EasyChair Preprint no. 12698

Abstract

In the era of digital transformation, retail businesses face increasing cybersecurity threats that demand innovative solutions for protection. This paper explores the convergence of business analytics, machine learning, and blockchain technology to fortify retail security. By leveraging advanced analytics, predictive modeling, and anomaly detection, businesses can proactively identify and mitigate potential threats. Machine learning algorithms enhance security measures by continuously learning from data patterns to detect and respond to emerging cyber threats in real-time. Additionally, integrating blockchain technology provides immutable and transparent transaction records, reducing the risk of data tampering and ensuring data integrity throughout the retail supply chain. This paper examines the synergistic potential of these technologies in bolstering retail cybersecurity and proposes strategies for their effective implementation.

Keyphrases: anomaly detection, Blockchain Technology, Business Analytics, Data Integrity, machine learning, predictive modeling, Retail Cybersecurity, Supply Chain Security

@Booklet{EasyChair:12698,
  author = {William Jack},
  title = {Fortifying Retail Security: Leveraging Business Analytics, Machine Learning, and Blockchain Integration for Enhanced Cyber Protection},
  howpublished = {EasyChair Preprint no. 12698},

  year = {EasyChair, 2024}}