Foundations of deep learning and large language models in cybersecurity

Author First name, Last name, Institution

Hewa Majeed Zangana, Duhok Polytechnic University
Marwan Omar, Illinois Institute of Technology
Jamal N. Al-Karaki, Zayed University

Document Type

Book Chapter

Source of Publication

Revolutionizing Cybersecurity with Deep Learning and Large Language Models

Publication Date

4-8-2025

Abstract

The integration of deep learning (DL) and large language models (LLMs) has significantly advanced the field of cybersecurity, offering innovative approaches to threat detection, anomaly identification, and secure communication. Deep learning techniques, such as neural networks and reinforcement learning, have demonstrated robust capabilities in detecting previously unknown threats by learning patterns from vast amounts ofcybersecurity data. Similarly,LLMs, particularly transformers, have revolutionized natural language processing tasks, enabling effective vulnerability analysis, malware classification, and phishing detection. This chapter explores the foundational concepts of deep learning and LLMs, highlighting their applications and challenges within the cybersecurity landscape. Additionally, it discusses the synergy between these technologies, focusing on how they complement traditional cybersecurity measures and drive the evolution of intelligent defense mechanisms.

DOI Link

10.4018/979-8-3373-3296-3.ch001

ISBN

[9798337332963, 9798337332987]

First Page

1

Last Page

35

Disciplines

Computer Sciences

Keywords

Deep learning, Large language models, Cybersecurity, Threat detection, Anomaly identification

Scopus ID

05004784249

Recommended Citation

Zangana, Hewa Majeed; Omar, Marwan; and Al-Karaki, Jamal N., "Foundations of deep learning and large language models in cybersecurity" (2025). All Works. 7312.
https://zuscholars.zu.ac.ae/works/7312

Indexed in Scopus

yes

Open Access

no