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GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networks — arXiv:2610.06517

Source: arXiv
Original: https://arxiv.org/abs/2610.06517
License (as archived): CC0-1.0
Archived: 2026-10-06T05:27:44.854Z
SHA-256 of the archived text: abd2ed734ee7ba4c4e7969441911740d62e8430d54e6c6e039c4eda983bdfd1d
Stable URL of this page: https://metraipolis.com/documento/dc_cb65274afea2

How to cite

GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networks — arXiv:2610.06517 — arXiv, https://arxiv.org/abs/2610.06517 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb65274afea2 (Metraipolis archive dc_cb65274afea2, sha256 abd2ed734ee7ba4c4e7969441911740d62e8430d54e6c6e039c4eda983bdfd1d)

BibTeX

@misc{dccb65274afea2,
  title = {GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networks — arXiv:2610.06517},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.06517},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb65274afea2; sha256 abd2ed734ee7ba4c4e7969441911740d62e8430d54e6c6e039c4eda983bdfd1d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.06517. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

IoT encompasses diverse physical entities, from smart home devices to autonomous vehicles, creating a complex environment with heterogeneous security models. This heterogeneity makes IoT sub-systems vulnerable to various network attacks. Modern security systems must therefore be more robust to ensure security and privacy for IoT applications. A highly secure IoT system also demands real time insight, requiring data collection at the edge of the computing layer. This diversity calls for a unified security model applied at the foundational level. Edge intelligence offers a direct approach to handling device diversity. A key goal of edge intelligence in IoT is to extract insight from local data; security models can then use this data to build local node protections, and integrating AI models yields an advanced security solution. This research proposes a novel deep learning algorithm for effective intrusion detection at the edge, supported by a cloud-based IoT framework. We evaluate the proposed cross modal deep learning algorithm against baseline models. The contribution is a cross domain Deep Neural Network (DNN) algorithm for intrusion detection. The objective is to assess a multi-method deep learning model to detect intrusions in IoT systems at the edge via community detection with modeled attention. We evaluate GCT auto-encoder, a novel framework integrating edge intelligence to identify security flaws. The model significantly improves performance and efficiency. On a network intrusion IoT dataset covering multiple attack scenarios, it achieved 0.908 accuracy, reduced learning loss to 0.00156, and outperformed existing approaches.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.06517 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:06.169Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).