Security Application

Security applications are increasingly leveraging machine learning, particularly large language models (LLMs) and vision transformers (ViTs), to automate tasks like intrusion detection, malware classification, and threat analysis. Current research emphasizes improving model explainability (XAI) to build trust and address concerns about adversarial attacks, focusing on techniques like prompt engineering for LLMs and ensemble methods for enhanced robustness. This work is significant because it promises to enhance efficiency and accuracy in cybersecurity, while simultaneously addressing critical issues of transparency and reliability in AI-driven security systems.