Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model
Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model
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DOI：
10.1109/cscloud-edgecom58631.2023.00043
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发表时间：
2023-07
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影响因子：
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通讯作者：
Souradip Roy;Juan Li;Yan Bai
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文献类型：
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作者：
Souradip Roy;Juan Li;Yan Bai
随着物联网（IoT）变得越来越普遍，对入侵检测系统（IDS）的需求也在增加，以防止网络攻击。然而，物联网设备有限的计算能力通常需要将数据发送到集中式云进行分析，这可能会导致能源消耗，隐私问题和数据泄漏。为了解决这些问题，我们提出了一个基于联合学习的IDS，它将学习分发到本地设备，而无需将数据发送到集中式云。我们还创建了轻量级的本地学习器来适应物联网设备的限制，并创建了本地适应的模型来处理非独立的入侵数据分布。我们使用NBaIoT和CICIDS-2017数据集评估了我们的方法，我们的结果表明，在准确性、精确度和召回率等指标上，我们的方法与集中式学习的性能相当，同时解决了隐私和数据泄露问题。
As the Internet of Things (IoT) becomes more prevalent, the need for intrusion detection systems (IDS) to protect against cyberattacks increases. However, the limited computing capabilities of IoT devices often require sending data to a centralized cloud for analysis, which can cause energy consumption, privacy issues, and data leakage. To address these problems, we propose a Federated Learning-based IDS that distributes learning to local devices without sending data to a centralized cloud. We also create lightweight local learners to accommodate IoT device limitations and locally adapted models to handle non-independent intrusion data distribution. We evaluate our method using NBaIoT and CICIDS-2017 datasets, and our results demonstrate comparable performance to centralized learning on metrics including accuracy, precision, and recall, while addressing privacy and data leakage concerns.