Exploring Ransomware Detection Based on Artificial Intelligence and Machine Learning

Abstract

Ransomware is an increasingly prevalent cybersecurity hazard due to its ability to encrypt data and request payment for its decryption. The threat’s dynamic nature generally renders conventional ransomware detection methods ineffective. This paper suggests an innovative method for detecting ransomware that capitalizes on artificial intelligence (AI) and machine learning (ML). A novel technique has been developed that integrates robust anomaly detection and classification algorithms with advanced feature extraction from system logs, network traffic, and file metadata. This technique achieves high accuracy with minimal false-positive rates by employing autoencoders, isolated forests for anomaly detection, random forests, and support vector machines for classification. The method’s ability to substantially improve ransomware defenses has been demonstrated through extensive testing on a large dataset, revealing that it outperforms current approaches. The study establishes a firm foundation for proactive ransomware detection and mitigation by demonstrating the advantages of integrating AI and ML in cybersecurity.