LATIDIA · Ciberseguridad

Análisis y mitigación de ataques de envenenamiento en la detección de intrusiones basada en flujo

arXiv: 2610.19766v1Tipo de anuncio: nuevo Resumen: Aunque los sistemas de detección de intrusiones (IDS)basados en inteligencia artificial (IA) se implementan cada vez más en redes operativas, su dependencia de la capacitación a gran escala d

La noticia

arXiv:2610.19766v1 Announce Type: new Abstract: Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on large-scale training data introduces a critical vulnerability to stealthy poisoning attacks. We demonstrate that latent backdoors can be successfully implanted in flow-based IDS models by poisoning as little as 0.001% of the training data, maintaining near-nominal performance on clean inputs while ensuring high attack success rates. To address this threat, we propose a diagnostic-first methodology that localizes trigger-relevant features through constrained perturbation analysis and detects poisoning-induced behavioral drift by comparison with a distilled baseline model. The proposed methodology further enables targeted mitigation via automated data sanitization or neuron