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Dr. M. V. Subramanyam

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Conference Jul 2026

Multi-Agent Reinforcement Learning System for Autonomous Network Intrusion Response

Cyber threats are becoming so complicated and rampant in today's highly connected digital world that there is a tremendous need for intelligent, adaptive IDS. Detecting and responding against network intrusions in real-time is the aim of the present work, which proposes a real-time Network Intrusion Detection and Autom...

S. Banu, D. Manogna, Farooq Sunar Mahammad et al. · 0 citations
Conference Jul 2026

Vision Transformer with Attention Rollout for Deepfake Face Image Detection and Localization

Generative AI and synthetic media generation tools have enabled widespread media manipulation tools and raised important privacy concerns with misinformation, identity fraud and the verification of authenticity of media. Most of the current convolution-based deepfake detection methods are hard to be deployed in real sc...

K. V. Sai Phani, Shaik Mahaboob Jailan, F. Mahammad et al. · 0 citations
Conference Jul 2026

Quantum Computing-based Optimization of Cybersecurity Protocols for Secure Data Protection

The escalation of security attacks has led to a growing complexity and posed serious challenges for traditional IDS paradigms to securing and sustaining a resilient network infrastructure. Current solutions are generally inefficient at optimizing detection parameters, cannot detect attacks that have previously not been...

M. Anusha, M. Neha, F. Mahammad et al. · 0 citations
Conference Jul 2026

Transformer-based Phishing Website Detection and Cyber Attack Prevention Systems

Phishing websites still pose a threat to internet users by using well-known domain names and confusing URL formats to trick them into divulging confidential information. This research proposes a phishing website detection system and a cyberattack prevention system based on a deep learning model designed to detect wheth...

Dr. N. Ramadevi, Dr. K. Uday, Kumar et al. · 0 citations
Conference Jul 2026

Autonomous LLM Agent for Real-Time DDoS Attack Classification and Mitigation Rule Generation

Large-scale DDoS attacks remain a serious threat to today's networked systems, which aim to make services unavailable by sending a massive amount of traffic. The traditional detection methods are mostly about attack categorization and are not that context-aware or actionable in providing support to security analysts. W...

K. V. Sai Phani, P. Karthik, Farooq Sunar Mahammad et al. · 0 citations

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