FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning
FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning
复制标题
DOI：
10.1109/infocom48880.2022.9796926
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Ning Wang;Yimin Chen;Yang Hu;W. Lou;Y. T. Hou
中科院分区：
文献类型：
--
作者：
Ning Wang;Yimin Chen;Yang Hu;W. Lou;Y. T. Hou
在过去的十年中，物联网（IoT）已经渗透到我们的日常生活中，应用范围广泛。然而，物联网设备缺乏足够的安全功能，使得物联网生态系统容易受到各种网络入侵攻击，可能造成严重损害。以前的工作已经探索了使用机器学习来构建异常检测模型，以抵御此类攻击。在本文中，我们提出了FeCo，这是一个联合对比学习框架，可以协调网络中的物联网设备共同学习入侵检测模型。FeCo利用联合学习来减轻用户的隐私问题，因为参与设备只提交其模型参数而不是本地数据。与以前的工作相比，我们开发了一种新的表示学习方法，基于对比学习，能够学习一个更准确的模型的良性类。FeCo显着提高了入侵检测的准确性相比，以前的作品。此外，我们实现了一个两步的特征选择方案，以避免过拟合和减少计算时间。通过在NSL-KDD数据集上的大量实验，我们证明了FeCo与最先进的技术相比，准确率提高了8%，并且对非IID数据具有鲁棒性。对收敛性、计算开销和可扩展性的评估进一步证实了FeCo对物联网入侵检测的适用性。
Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of sufficient security features in IoT devices renders IoT ecosystems vulnerable to various network intrusion attacks, potentially causing severe damage. Previous works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. Besides, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-IID data. Evaluations on convergence, computation overhead, and scalability further confirm the suitability of FeCo for IoT intrusion detection.