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Immutable Adversarial Attack Evidence Collection for ML Cyber Defense using Blockchain
Advanced cybersecurity issues that modern banking applications face include SQL injection, cross-site scripting, adversarial machine learning attacks, and attempts to compromise credentials. Conventional logging methods undermine incident investigation and regulatory compliance due to mutability issues and a lack of forensic integrity. This study offers a novel architecture that creates immutable evidence collection for cyber defense by fusing blockchain technology with machine learning-based threat detection. The suggested system automatically logs attack evidence on a private blockchain ledger and uses Random Forest classifiers to detect malicious activity in real-time. The implementation makes use of MongoDB for operational data storage, Python based ML microservices, Node.js backend orchestration, and React.js frontend. Using cryptographic verification, smart contracts guarantee the persistence of unchangeable evidence. Experimental validation demonstrates 96% detection accuracy across various attack vectors with sub-second response latency. The blockchain-anchored evidence provides non-repudiable forensic trails suitable for audit and legal proceedings, addressing critical gaps in conventional security information management systems.
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