Prabhudarshi Nayak

Prabhudarshi Nayak, Gogulakrishnan Thiyagarajan, Debashree Priyadarshini et al.

Ransomware continues to evolve as one of the most disruptive cyber threats, with recent variants increasingly leveraging automated and AI-assisted techniques to evade traditional signature-based defenses. Early detection of such attacks remains a significant challenge, particularly when malicious behavior closely resembles legitimate system activity. This study proposes an explainable attention-based Long Short-Term Memory (LSTM) framework for the early detection of AI assisted ransomware variants through analysis of file system behavioral patterns. The proposed model captures temporal dependencies in file operation sequences, while an attention mechanism highlights critical behavioral indicators associated with ransomware activity. To improve transparency and trust in automated detection systems, explainable artificial intelligence (XAI) techniques are incorporated to interpret model predictions and identify influential behavioral features. Experimental evaluation using ransomware behavioral traces demonstrates that the proposed framework can effectively distinguish malicious activity at early stages of execution with high detection performance and low false-positive rates. The findings suggest that combining sequence-aware deep learning models with explainability mechanisms can significantly enhance the reliability and interpretability of next-generation ransomware defense systems. This work contributes toward the development of intelligent and transparent cyber-defense mechanisms capable of addressing emerging AI-driven malware threats.

Prabhudarshi Nayak, Gogulakrishnan Thiyagarajan, Ritunsa Mishra et al.

Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.