arXiv:2606.29797v1 Announce Type: cross Abstract: Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggregated flow datasets. We propose Multi-Level Distributional Entropy (MDE), an analytical framework that derives interpretable entropy features directly from flow-level summary statistics at three levels: within-flow Gaussian differential entropy, cross-directional Jensen-Shannon divergence (JSD), and Transmission Control Protocol
The increasing sophistication and frequency of cyber attacks necessitate more advanced and interpretable intrusion detection systems, pushing for innovations in AI/ML applications in cybersecurity.
This development offers a novel approach to network intrusion detection, potentially enhancing the ability to identify complex threats by extracting richer, more interpretable features from network traffic.
Traditional reliance on aggregate flow statistics is challenged by a method that incorporates deeper distributional structure, providing more granular insights for threat analysis and making AI-driven IDS more explainable.
- · Cybersecurity firms
- · Organizations with critical infrastructure
- · AI/ML in cybersecurity researchers
- · Cyber attackers
- · Legacy network intrusion detection systems
Improved detection rates for novel and sophisticated network intrusions will reduce the financial and operational impact of cyber attacks.
The explainability of MDE could lead to faster incident response and better understanding of attack vectors, potentially shaping new cybersecurity protocols.
More resilient and secure digital infrastructure could accelerate the adoption of advanced networked technologies across various sectors, relying on trusted AI defenses.
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Read at arXiv cs.LG