Smart surveillance methodology: Utilizing machine learning and AI with blockchain for bitcoin transactions

The combination of artificial intelligence (AI) and blockchain technology is changing surveillance systems by increasing security and operational efficiency. This study looks into a smart surveillance methodology that uses machine learning and artificial intelligence to analyze Bitcoin transactions in a blockchain context. The major purpose is to assess the performance of three machine learning algorithms in detecting anomalies and categorizing transactions: Gaussian Naive Bayes (Gaussian NB), Random Forest Classifier, and Decision Tree Classifier. AI allows for real-time data processing and proactive threat detection, while blockchain assures data integrity and transparency. These technologies are designed to improve situational awareness, secure data sharing, and optimize surveillance operations. The study entails gathering Bitcoin transaction data, preprocessing to address missing values, standardization, and feature extraction, and then applying the chosen machine learning methods. Metrics used to assess performance include accuracy, precision, recall, and the F1-score. The results reveal that the Random Forest Classifier surpasses the other algorithms in terms of improving the security and efficiency of smart surveillance systems. This study fills a significant gap by providing empirical evidence for the use of machine learning in blockchain-based surveillance. The findings demonstrate the possibility of combining AI and blockchain technology to create robust and secure monitoring tools.