A Theoretical Framework for Adversarial Robustness in Real-Time ML-Based Intrusion Detection Systems (IDS)

Machine learning-based intrusion detection systems (IDS) are now commonly used in real-time cybersecurity to defend against quickly changing threats. However, recent developments in adversarial machine learning have shown that many IDS models are still very susceptible to adaptive attacks, especially in real-time conditions. Most existing research examines adversarial robustness in offline or static scenarios, missing the dynamic nature of live network traffic, ongoing data flows, and strict time requirements. This gap limits the effectiveness of current adversarial defense strategies in practical intrusion detection systems.
This paper aims to fill this gap by proposing a theoretical framework for adversarial robustness in real-time machine learning-based intrusion detection systems. The framework treats adversarial robustness as a characteristic that changes over time, influenced by detection delays, attacker strategies, concept drift, and ongoing interactions between models and adversaries. We introduce formal concepts such as time-to-evasion, detection stability, and robustness decay to describe how IDS reacts under lasting adversarial pressure.
Instead of creating a new detection algorithm, this study gives a theoretical viewpoint that clarifies why many adversarial defenses perform well in offline tests but struggle in real-time scenarios. The framework applies to various IDS designs and machine learning methods. By connecting adversarial machine learning theory with the needs of real-time intrusion detection, this work lays the groundwork for future testing, comparisons, and development of resilient IDS for challenging operational environments.

Adversarial Machine Learning; Intrusion Detection Systems; Real-Time Cybersecurity

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