Natural Language Processing (NLP) for Threat Intelligence
Rajesh Kanna Rajendran, B Tulasi · Advances in information security, privacy, and ethics book series · 2024
The integration of NLP into threat intelligence systems offers numerous advantages. Automated analysis significantly reduces the time and effort required by human analysts, allowing them to focus on higher-level strategic tasks. Besides, NLP models can process information in real-time, providing timely alerts and insights that are essential for proactive defense measures. By continuously learning from new data, these models improve over time, enhancing their accuracy and relevance.The application of NLP in threat intelligence also presents challenges. The dynamic and evolving nature of language, especially in the context of cybersecurity, requires continuous model updates and refinements. Additionally, the presence of jargon, slang, and coded language in cyber threat communications necessitates sophisticated linguistic models capable of deep contextual understanding. Ensuring data privacy and addressing ethical concerns related to surveillance and information gathering are also critical considerations.