Learning to Secure: A Survey on Meta-Learning Approaches in Cyber Threat Detection and Response

Al-Araji, Zaid

doi:10.69513/jncs.v2.i2.a1

	Learning to Secure: A Survey on Meta-Learning Approaches in Cyber Threat Detection and Response
Al-Noor Journal for Information Technology and Cybersecurity
Article 1, Volume 2, Issue 2, December 2025, Pages 1-7 PDF (355.37 K)
Document Type: Review Article
DOI: 10.69513/jncs.v2.i2.a1
Author
Zaid Al-Araji^*
Ninevah University, Mosul, Iraq
Abstract
The increasing sophistication and dynamism of cyber threats demand security systems that can adapt rapidly to new and evolving attack patterns. Meta-learning, or "learning to learn," offers a promising paradigm for enhancing the adaptability and generalization of machine learning models in cybersecurity contexts. This survey presents a review of recent research on meta-learning approaches applied to cyber threat detection and response, with a particular focus on intrusion detection systems, malware classification, phishing detection, anomaly detection, and adversarial defense. We categorize existing methods into optimization-based, metric-based, and model-based meta-learning, and examine their strengths in few-shot learning, task generalization, and robustness under domain shifts. Furthermore, we identify key challenges, including the lack of standardized benchmarks, computational overhead, explainability limitations, and vulnerability to adversarial attacks. By synthesizing recent advances and outlining open research questions, this paper aims to guide future developments in adaptive, intelligent cybersecurity systems by using meta-learning to enhance the attack detection or even to protect the systems.
Keywords
Meta-Learning; Security; IDS; Cyber Threat Detection; Learn to Learn

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