Blockchain Forensics: Detecting and Mitigating Malicious Transactions Through AI and Pattern Analysis

Abstract

The blockchain is among such technologies that have gained the most extraordinary popularity due to cryptocurrencies and are used in different industries because of their decentralization and immutability. This aspect however also introduces the aspect of fraudulent transactions and to curb this aspect well-developed forensics is required to trace the ill motived transactions and neutralize them. Blockchain forensics is concerned with crim investigation, tracking, and prevention, using analysis of data on a blockchain. In some cases, standard approaches just are not enough, transactions are complex as well as large in size. The given paper proposes a technique based on Artificial Intelligence (AI) and pattern analysis methods to make the process of identifications and prevention of malicious transactions in a blockchain more effective. AI can identify complicated pattern and outliers that would have otherwise been undetectable with machine learning models. The research proposal focuses on AI in blockchain forensics, identifying pattern recognition methods, and evaluating the efficiency of these methods in practice. The paper has also suggested that detection of fraudulent transactions, including double-spending, transaction laundering, and phishing attacks, can be accomplished through a new AI-based approach. It has established that AI models hold a great promise of making blockchain forensics more precise and effective and result in quicker and more reliable detecting systems. This paper is relevant to the on-going research in Making blockchain networks secure since the research entails a combined strategy in the detection and prevention of malicious practices.