{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T11:47:24Z","timestamp":1776426444248,"version":"3.51.2"},"reference-count":20,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cryptography"],"abstract":"<jats:p>While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding\u2014an AI-assisted development and analysis methodology that allows for the \u2018encoding\u2019 and interpretation of the dynamic \u2018vibe\u2019 or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed\u2013Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures.<\/jats:p>","DOI":"10.3390\/cryptography10020025","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:20:16Z","timestamp":1775816416000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Chaos Theory with AI Analysis in IoT Network Scenarios"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6399-9032","authenticated-orcid":false,"given":"Antonio Francesco","family":"Gentile","sequence":"first","affiliation":[{"name":"Institute for High Performance Computing and Networking Italian National Research Council (ICAR-CNR), Via P. Bucci, 87036 Rende, Italy"}]},{"given":"Maria","family":"Cilione","sequence":"additional","affiliation":[{"name":"Independent Researcher, 00133 Roma, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/TVT.2024.3453450","article-title":"On the Effect of Coverage Range Extent on Next-Cell Prediction Error for Vehicular Mobility in 5G\/6G Networks: A Novel Theoretic Model","volume":"74","author":"Fazio","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gentile, A.F., and Cilione, M. (2026). Chaos Theory with AI Analisys in Network Scenarios. 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