AI for Network Security

Dilli Prasad Sharma (), Arash Habibi Lashkari (), Mahdi Daghmehchi Firoozjaei (), Samaneh Mahdavifar () and Pulei Xiong ()
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Dilli Prasad Sharma: University of Toronto
Arash Habibi Lashkari: York University
Mahdi Daghmehchi Firoozjaei: MacEwan University
Samaneh Mahdavifar: McGill University
Pulei Xiong: National Research Council of Canada

Chapter Chapter 4 in Understanding AI in Cybersecurity and Secure AI, 2025, pp 55-68 from Springer

Abstract: Abstract The rapid evolution of network architectures has introduced new security challenges, necessitating advanced network protection solutions. This chapter explores the evolution of network security, from traditional firewalls and intrusion detection/prevention systems (IDS/IPS) to modern AI-driven network security solutions. It examines key threat detection approaches, including signature-based, anomaly-based, policy-based, and reputation-based detection, and how AI and machine learning (ML) have transformed network security by enabling automated, adaptive, and real-time threat response mechanisms. The chapter also discusses the future of AI in network security, emphasizing its role in predictive analytics, behavioral analysis, and automated response systems. Additionally, it highlights the challenges and risks associated with AI-based security, such as adversarial attacks, data quality limitations, and integration issues, underscoring the need for continuous advancements in AI-driven defense strategies.

Date: 2025
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DOI: 10.1007/978-3-031-91524-6_4

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