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<p>Deep learning has generated strong results for network intrusion detection, but much of the literature still treats the problem as static offline classification, leaving unclear how such models should be conceived for deployment. In practice, intrusion detection must learn from traffic streams, cope with previously unseen attacks, and exploit heterogeneous evidence sources without relying on bulky preprocessing or heavyweight models. This paper addresses that conceptual gap by introducing <span>SmartVille</span>, a framework for <i>formulating</i> and <i>studying</i> deep learning-based NID under online, open-world, and multi-modal assumptions. The main contribution is therefore not a new stand-alone detection algorithm, but a coherent research blueprint that integrates existing learning principles into a single technical vision. In particular, <span>SmartVille</span> advocates an end-to-end differentiable encode–process–decode organisation, in which neural encoders replace bulk feature engineering, online learning replaces static train-once evaluation, and collective anomaly detection is studied alongside supervised classification within the same framework. This perspective clarifies what <span>SmartVille</span> specifically solves: it provides a principled way to design, train, and benchmark adaptive NID models under realistic assumptions, while separating the theoretical contribution from its open-source implementation. Representative use cases show how the framework can be used to analyse curriculum design, input-modality composition, and architectural choices for adaptive intrusion detection research.</p>

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SmartVille: A Framework for Realistic Deep Learning-Based Online Network Intrusion Detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Jesús F. Cevallos Moreno,
  • Alessandra Rizzardi,
  • Sabrina Sicari,
  • Alberto Coen-Porisini

摘要

Deep learning has generated strong results for network intrusion detection, but much of the literature still treats the problem as static offline classification, leaving unclear how such models should be conceived for deployment. In practice, intrusion detection must learn from traffic streams, cope with previously unseen attacks, and exploit heterogeneous evidence sources without relying on bulky preprocessing or heavyweight models. This paper addresses that conceptual gap by introducing SmartVille, a framework for formulating and studying deep learning-based NID under online, open-world, and multi-modal assumptions. The main contribution is therefore not a new stand-alone detection algorithm, but a coherent research blueprint that integrates existing learning principles into a single technical vision. In particular, SmartVille advocates an end-to-end differentiable encode–process–decode organisation, in which neural encoders replace bulk feature engineering, online learning replaces static train-once evaluation, and collective anomaly detection is studied alongside supervised classification within the same framework. This perspective clarifies what SmartVille specifically solves: it provides a principled way to design, train, and benchmark adaptive NID models under realistic assumptions, while separating the theoretical contribution from its open-source implementation. Representative use cases show how the framework can be used to analyse curriculum design, input-modality composition, and architectural choices for adaptive intrusion detection research.