# Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection

arXiv: https://arxiv.org/abs/2409.03141 · HTML: https://mlsanity.com/paper/2409.03141/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network](https://mlsanity.com/paper/2212.01298/) (2022)
- [Regularized impurity reduction: Accurate decision trees with complexity guarantees](https://mlsanity.com/paper/2208.10949/) (2022)

## Built on by (most cited first, 5 of 5)

- [Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System](https://mlsanity.com/paper/2511.08491/) (2025)
- [Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework](https://mlsanity.com/paper/2604.20288/) (2026)
- [Synthetic Flight Data Generation Using Generative Models](https://mlsanity.com/paper/2604.20293/) (2026)
- [A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks](https://mlsanity.com/paper/2608.02274/) (2026)
- [An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2609.23097/) (2026)

## Abstract (arXiv)

The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have shown effectiveness in mitigating these risks, but they often require extensive manual effort and expert knowledge. To address these challenges, this paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. To achieve autonomous intrusion detection, the proposed AutoML framework automates all critical procedures of the data analytics pipeline, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Specifically, it utilizes a Tabular Variational Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models for automated feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and a novel Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance compared to state-of-the-art cybersecurity methods. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.