Securing Smart Networks and Privacy Intrusion Detection System Utilizing Blockchain and Machine Learning

Abstract

The digital landscape is facing threats from malicious actors, including malware, phishing, ransomware, and distributed denial-of-service attacks. This article introduces the Hybrid Intelligent Random Forest (HIRF) method, which combines machine learning and blockchain technology to detect anomalies in digital environments. HIRF has demonstrated exceptional accuracy rates in identifying and forecasting cyber-attacks, achieving 99.53% for the KD99 dataset and 99.53% for the UNBS-NB 15 dataset. It also minimizes false positives and negatives, enhancing network efficiency. HIRF's scalability and performance effectiveness make it suitable for government and business sectors, where it can enhance security measures and protect digital infrastructure against evolving cyber threats. Its potential applications extend to government and business sectors, where it can be instrumental in bolstering security measures and fortifying digital infrastructure against cyber threats.