• Yuvashri M
• Vijayakumar S

Ransomware attacks have become a pervasive and evolving threat in the digital landscape, demanding innovative defensive strategies to protect organizations and individuals. Ransomware creators continually refine their tactics, rendering traditional signature based detection methods ineffective. Additionally, ransomware is often delivered through a variety of vectors including phishing emails, malicious attachments and compromised websites. Detecting and thwarting these diverse delivery methods presents a significant challenge. This proposed method introduces a proactive defense strategy against ransomware threats, leveraging “RanGAN (Ransomware Generative Adversarial Network)” for early detection and “Hash Conceal” for data protection. RanGAN employs advanced machine learning to detect ransomware behavior patterns in real time, while Hash Conceal secures critical data from malicious encryption. So we can provide a robust defense, ensuring rapid threat identification and minimizing data loss. By using a real time ransomware sample, the experiment result shows that our proposed method protects the files from the attack in an effective manner.

Ransomware, Cyber-security, Defense mechanism, Machine Learning

"Proactive Anti Agent Against Ransomware Threats Using RanGAN And Hash Conceal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.f495-f500, January-2024, Available :http://www.jetir.org/papers/JETIR2401558.pdf

"Proactive Anti Agent Against Ransomware Threats Using RanGAN And Hash Conceal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppf495-f500, January-2024, Available at : http://www.jetir.org/papers/JETIR2401558.pdf