Resource-Aware Layered Intrusion Detection Allocation Model

Computer Science > Cryptography and Security [Submitted on 24 Apr 2026] Resource-Aware Layered Intrusion Detection Allocation Model Ioan Pădurean, Béla Genge, Roland Bolboacă 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. Comments: 6 pages, 2 figures, for conference publication Subjects: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2604.22304 [cs.CR]   (or arXiv:2604.22304v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.22304 Focus to learn more Submission history From: Ioan Pădurean [view email] [v1] Fri, 24 Apr 2026 07:36:23 UTC (65 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)