English(EN) Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints

AI模型在资源受限环境下进行物联网DDoS检测的评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

一项新的研究论文评估了在资源受限的物联网（IoT）环境中检测分布式拒绝服务（DDoS）攻击的几种预训练卷积神经网络架构。该研究使用CICDDoS2019数据集和基于图像的流量表示，根据准确性、可靠性、计算成本和可解释性评估了模型。DenseNet和MobileNet架构表现出强大的性能和可靠性，其中MobileNet显示出高准确性，而DenseNet169提供了卓越的定性可解释性。 AI

影响强调了在为关键基础设施安全部署AI时，除了准确性之外，还需要考虑可解释性和可靠性的重要性。

排序理由在arXiv上发表的研究论文，详细介绍了对特定任务的AI模型的实证评估。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Nelly Elsayed · 2026-10-31 04:00

面向资源受限物联网DDoS检测的可解释性感知迁移学习模型评估

arXiv:2602.22488v2 Announce Type: replace-cross Abstract: Distributed denial of service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have show…

本文如何被排名

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面向资源受限物联网DDoS检测的可解释性感知迁移学习模型评估

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