ArXiv TLDR

Resource-Aware Layered Intrusion Detection Allocation Model

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2604.22304

Ioan Pădurean, Béla Genge, Roland Bolboacă

cs.CRcs.NI

TLDR

This paper proposes a resource-aware integer linear program to optimize layered intrusion detection monitoring depth across heterogeneous networks, balancing costs and detection.

Key contributions

  • Proposes an integer linear program for optimal layered intrusion detection allocation.
  • Considers device importance, attack probability, detection rates, and monitoring costs.
  • Enforces global resource budgets, critical device minimums, and constrained device limits.
  • Illustrates resource concentration on important/high-risk devices in a small network.

Why it matters

This model provides a systematic way to optimize intrusion detection in complex networks, balancing advanced threat detection with limited resources. It helps organizations deploy IDS more efficiently, focusing efforts where they matter most.

Original Abstract

This paper proposes a resource-aware allocation model for layered intrusion detection in het erogeneous networks. Monitoring traffic at higher protocol layers improves the ability to detect sophisticated attacks, but it also increases computational and storage costs. The problem is formu lated as an integer linear program that assigns a single monitoring depth, ranging from Ethernet to the application layer, to each device, while accounting for device importance, attack probability, layer-dependent detection rates, and per-layer monitoring costs. The model further enforces a global resource budget, a minimum monitoring level for critical devices, and maximum-feasibility limits for constrained devices such as simple IoT sensors. The formulation is solved with the SCIP optimization framework on a small heterogeneous network of six devices, and the resulting allocation illustrates how the model concentrates monitoring effort on important and high-risk devices while respecting feasibility and budget constraints.

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