Natural Language Processing for Cybersecurity Incident Analysis

EasyChair Preprint 14341

Abstract

In the rapidly evolving landscape of cybersecurity, the ability to efficiently analyze and respond to incidents is critical. Natural Language Processing (NLP) offers powerful tools and methodologies to enhance the analysis, detection, and mitigation of cybersecurity incidents. This paper explores the application of NLP techniques in cybersecurity incident analysis, focusing on several key areas: threat intelligence, incident response, and automated reporting.

Firstly, we discuss the role of NLP in extracting valuable insights from unstructured data sources, such as security logs, threat reports, and online forums. NLP techniques, including named entity recognition (NER) and sentiment analysis, enable the identification of relevant entities and the assessment of their potential threat levels.

Secondly, we delve into the automation of incident response through NLP-driven chatbots and virtual assistants, which can triage incidents, provide real-time support, and facilitate communication among response teams. These tools leverage NLP to understand and generate human-like responses, significantly reducing the response time and improving accuracy.

Keyphrases: Cyber Security, learning, machine

@booklet{EasyChair:14341,
  author    = {Obaloluwa Ogundairo and Peter Broklyn},
  title     = {Natural Language Processing for Cybersecurity Incident Analysis},
  howpublished = {EasyChair Preprint 14341},
  year      = {EasyChair, 2024}}