Foundations of deep learning and large language models in cybersecurity
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
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
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