Work

Automated Network Defense: A Systematic Survey and Analysis of AutoML Paradigms for Network Intrusion Detection

Year: 2025

Type: article

Abstract: As cyberattacks grow increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) have become essential for securing cyberspace. While Machine Learning (ML) is foundational to modern NIDS, its effectiveness is often hampered by a resource-intensive development pipeline involving feature engineering, model selection, and hyperparameter tuning. Automated Machine Learning (AutoML) promises a solution, but its application to the massive, high-speed data streams in NIDS is fundamentally a parallel and distributed computing challenge. This paper argues that the scalability and performance of AutoML in NIDS are governed by the underlying computational paradigm. We introduce a novel taxonomy of AutoML frameworks, uniquely classifying them by their parallel and distributed architectures. Through a comprehensive meta-analysis of over 15 NID methods on benchmark datasets, we demonstrate how the performance of leading systems is a direct consequence of their chosen computational paradigm. Finally, we identify frontier challenges and future research directions at the intersection of AutoML, NIDS, and high-performance distributed systems, focusing on computational scalability, security, and end-to-end automation.

Source: Applied Sciences

DOI: 10.3390/app151910389

Authors: Haowen Liu, Xuren Wang, Famei He, Zhiqiang Zheng

Institutions: Capital Normal University, Ministry of Education of the People's Republic of China, Beijing Institute of Technology

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Ministry of Education of the People's Republic of China