# Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System

arXiv: https://arxiv.org/abs/2511.08491 · HTML: https://mlsanity.com/paper/2511.08491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022)
- [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022)
- [Automated Machine Learning for Deep Learning based Malware Detection](https://mlsanity.com/paper/2303.01679/) (2023)
- [Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection](https://mlsanity.com/paper/2409.03141/) (2024)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Zero-Touch Networks: Towards Next-Generation Network Automation](https://mlsanity.com/paper/2312.04159/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks](https://mlsanity.com/paper/2608.02274/) (2026)

## Abstract (arXiv)

With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things (IoT) systems amplifies these challenges, as resource-constrained IoT devices demand scalable and efficient security solutions. In this work, an innovative Intrusion Detection System (IDS) utilizing Automated Machine Learning (AutoML) and Multi-Objective Optimization (MOO) is proposed for autonomous and optimized cyber-attack detection in modern networking environments. The proposed IDS framework integrates two primary innovative techniques: Optimized Importance and Percentage-based Automated Feature Selection (OIP-AutoFS) and Optimized Performance, Confidence, and Efficiency-based Combined Algorithm Selection and Hyperparameter Optimization (OPCE-CASH). These components optimize feature selection and model learning processes to strike a balance between intrusion detection effectiveness and computational efficiency. This work presents the first IDS framework that integrates all four AutoML stages and employs multi-objective optimization to jointly optimize detection effectiveness, efficiency, and confidence for deployment in resource-constrained systems. Experimental evaluations over two benchmark cybersecurity datasets demonstrate that the proposed MOO-AutoML IDS outperforms state-of-the-art IDSs, establishing a new benchmark for autonomous, efficient, and optimized security for networks. Designed to support IoT and edge environments with resource constraints, the proposed framework is applicable to a variety of autonomous cybersecurity applications across diverse networked environments.