LLMs for Quantum-Aware Threat Detection and Incident Response

View Sample PDF

Author(s): Luay Albtosh (Capitol Technology University, USA & Houston Community College, USA)
Copyright: 2025
Pages: 38
Source title: Leveraging Large Language Models for Quantum-Aware Cybersecurity
Source Author(s)/Editor(s): Hewa Majeed Zangana (Duhok Polytechnic University, Iraq)and Marwan Omar (Illinois Institute of Technology, USA)
DOI: 10.4018/979-8-3373-1102-9.ch004

Keywords: Cyber and Network Security / Information Science Reference / Knowledge Management and Library Science / Natural Language Processing

Purchase

View LLMs for Quantum-Aware Threat Detection and Incident Response on the publisher's website for pricing and purchasing information.

Abstract

The integration of Large Language Models (LLMs) into cybersecurity offers significant potential for enhancing threat detection and incident response, particularly in the context of emerging quantum computing capabilities. As quantum computing threatens to undermine traditional cryptographic systems, there is a pressing need for quantum-aware cybersecurity measures. This chapter explores the application of LLMs in identifying and mitigating threats in a quantum-aware environment. It examines how LLMs can aid in detecting quantum-related vulnerabilities, analyzing quantum-aware attack vectors, and automating incident response protocols. Additionally, the chapter discusses the challenges of adapting LLMs to the quantum landscape, including the need for specialized training datasets and the consideration of quantum-resistant algorithms. Through case studies and theoretical models, this chapter provides a comprehensive view of how LLMs can be leveraged to strengthen cybersecurity in the era of quantum computing.

The IRMA Community

Research IRM

LLMs for Quantum-Aware Threat Detection and Incident Response

Purchase

Abstract

Related Content

IRMA Sponsors