AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper presents a comprehensive and systematic review of deep learning techniques applied to cyber intrusion detection within IoV systems, conducted in accordance with the PRISMA framework across 83 selected studies published between 2020 and 2025.
A key contribution of this survey is the introduction of explicit and consistent criteria for labeling IDSs as lightweight, real-time, or deployable, based only on substantiated evidence such as ECU-oriented runtime analysis, embedded evaluation, or real-vehicle experimentation.
In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to...
The fast-growing interconnectivity of networks and digital communication platforms, along with extensive information exchange, has made cybersecurity issues in modern computing environments more severe. The expansion of networking infrastructures always results in a massive flow of traffic, thus rendering traditional m...
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Significant performance drops are observed, including CPU exhaustion and denial of service for regular users during volumetric fragmentation attacks, showing that organizations must constantly refine their firewall policies and look into kernel-bypass technologies to keep their ICS networks running smoothly.
Per-ID behavioral residualization is presented, a CAN-specific representation that extracts fourteen temporal, protocol, and payload features from sliding windows and residualizes them against each arbitration ID's normal baseline, which improves mean F1 in the majority of evaluations.
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