FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks
FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks
复制标题
FlowGuard：针对物联网 DDoS 攻击的智能边缘防御机制
DOI：
10.1109/jiot.2020.2993782
复制
发表时间：
2020-10-01
影响因子：
10.6
通讯作者：
Cheng, Xiuzhen
中科院分区：
文献类型：
--
作者：
Jia, Yizhen;Zhong, Fangtian;Cheng, Xiuzhen
近年来，物联网（IoT）设备越来越流行，物联网网络在行业和人们的活动中发挥着重要作用。一方面，它们给我们日常生活的各个方面带来了便利;另一方面，它们容易受到各种攻击，从而在一定程度上抵消了它们的好处。在本文中，我们针对物联网分布式拒绝服务（DDoS）攻击的防御技术，并提出了一个以边缘为中心的物联网防御方案FlowGuard，用于检测，识别，分类和缓解物联网DDoS攻击。提出了一种新的基于流量变化的DDoS攻击检测算法，并设计了两种用于DDoS攻击识别和分类的机器学习模型。为了证明这两种机器学习模型的有效性，我们通过DDoS模拟器BoNeSi和SlowHTTPTest生成了一个大数据集，并将其与CICDDoS 2019数据集相结合，以测试识别和分类的准确性以及模型的效率。我们的研究结果表明，所提出的长短期记忆的识别准确率高达98.9%，这显着优于其他四个著名的学习模型中提到的最相关的工作。所提出的卷积神经网络的分类准确率高达99.9%。此外，当部署在计算能力高于个人计算机的边缘服务器上时，我们的模型令人满意地满足了物联网的延迟要求。
Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people's activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.