Phishing attacks remain a significant cybersecurity threat, deceiving individuals into revealing sensitive information via counterfeit websites. Traditional anti-phishing methods, often centralized and reactive in nature, have critical shortcomings. These systems are plagued by slow detection, poor adaptability to evolving threats, vulnerabilities to data manipulation, and a lack of transparency. To address these issues, a new approach known as MLPhishChain has been proposed—an innovative decentralized application (DApp) that integrates machine learning (ML) and blockchain technology for proactive phishing prevention.
MLPhishChain offers a tamper-proof, automated solution for URL verification by utilizing an ML model to analyze and assess the phishing potential of websites. Each analysis is recorded on a secure blockchain ledger, ensuring that the data is immutable and transparent. Unlike centralized systems, which are prone to tampering, MLPhishChain guarantees the integrity of its verification results. Furthermore, the framework includes a unique re-evaluation mechanism, allowing users to request updated assessments of URLs as website content evolves. This feature enhances the system’s ability to stay relevant in the face of dynamic phishing tactics. To further bolster its reliability, MLPhishChain also aggregates data from external security sources, such as VirusTotal, offering a multi-layered validation process for users. This approach ensures that users receive a well-rounded analysis, allowing them to make informed decisions about the safety of URLs they encounter. By combining machine learning’s intelligence with blockchain’s decentralized security, MLPhishChain sets a new benchmark for phishing detection and prevention.
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