MALWARE OPTIMIZATION DETECTION USING VARIOUS ADVANCED THREATS FOR TOXIC COMMENT ANALYSIS

Abstract

The Malware optimization detection using various advanced threads of toxic comment analysis using an algorithm, namely Double Deep Q-Network (DDQN) with Bi-LSTM of cybersecurity in malware with NLP of toxic comments. With increasing cyber threats and harmful online interfaces, there are dual challenges for the digital ecosystem. The malware infiltration and toxic communication often coexist to compromise trust, privacy, and user safety. The traditional detection model treats these domains in separate fields. The main result is to limit against rapid evolving attacks. This study proposed a hybrid work of Double Deep Q-Networks (DDQN) for optimized malware detection and Bi-directional Long Short-Term Memory (BiLSTM) for toxic comment analysis. The integration with the decision-making system. The DDQN enhanced threat prediction through reinforcement learning by dynamically optimizing detection strategies of polymorphic and zero-day malware. The bi-LSTM module captures contextual dependencies in online text that enable robust identification of subtle, disguised, and adversarial toxic expressions. The practical evaluation demonstrates that the combined model achieved higher precision and adaptability compared to standalone approaches. The offering of a scalable defense mechanism across heterogeneous platforms. The malware intrusions and toxic interactions by fostering a secure and trustworthy cyber environment. The overall hybrid accuracy of 93.5 % of average across domains and has higher robustness than individual models.