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

2025-11-11

IEEE Transactions on Machine Learning in Communications and Networking

AI Summary

To address the need for autonomous, efficient, and robust intrusion detection in resource-constrained environments—such as IoT and edge devices—this paper proposes a novel intrusion detection system (IDS) framework integrating AutoML with multi-objective optimization. The method achieves, for the first time, end-to-end co-optimization across the entire AutoML pipeline: feature selection, algorithm selection, hyperparameter tuning, and model evaluation. It introduces two key innovations: OIP-AutoFS—an Optimality-based Importance and Percentile-driven automatic Feature Selection method—and OPCE-CASH—a Performance-, Confidence-, and Efficiency-aware algorithm and hyperparameter search scheme. Evaluated on two benchmark datasets, the framework consistently outperforms state-of-the-art IDSs, achieving Pareto-optimal trade-offs among detection accuracy, inference latency, and model confidence. This work establishes a scalable, lightweight paradigm for autonomous network security protection in constrained-edge settings.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Developing autonomous cybersecurity for modern networks using AutoML
• Optimizing intrusion detection for resource-constrained IoT systems
• Balancing detection effectiveness with computational efficiency through multi-objective optimization

Innovation

• Automated Machine Learning for intrusion detection
• Multi-objective optimization balances effectiveness and efficiency
• Integrated AutoML stages with feature and model optimization