Abstract: Ransomware attacks have evolved into one of the most significant cyber threats, causing extensive financial and reputational damage to organizations across the globe. Traditional security measures have proven inadequate in detecting and preventing the increasingly sophisticated techniques used by ransomware attackers. This paper explores the role of artificial intelligence (AI) and machine learning (ML) in combating ransomware by analyzing network traffic patterns, identifying malware signatures, and predicting potential threats before they fully manifest. The research highlights key AI and ML methodologies, such as anomaly detection, supervised learning algorithms, and natural language processing (NLP), that help cybersecurity systems improve accuracy in identifying malicious activity. Furthermore, the paper discusses the integration of AI-driven systems in incident response frameworks and the advantages these technologies offer in automating defenses against ransomware. Despite the promise of AI and ML in enhancing cybersecurity, challenges such as false positives, adversarial attacks, and ethical considerations in AI deployment are also discussed. This paper concludes with recommendations for future research and development to further enhance the effectiveness of AI in ransomware prevention.
Keywords: Ransomware, artificial intelligence, machine learning, anomaly detection, malware signatures, predictive models, cybersecurity, adversarial attacks, incident response, automation in security.
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