arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.00296cs.CR
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通过软件定义网络和低成本流量分类保护灵巧家庭

Securing Smart Homes via Software-Defined Networking and Low-Cost Traffic Classification

Holden Gordon, Christopher Batula, Bhagyashri Tushir, Behnam Dezfouli, Yuhong Liu

摘要:

物联网设备由于缺乏行业范围的安全标准,已成为各种网络攻击的热门目标。在这项工作中,我们专注于灵巧家庭物联网设备识别和防御分布式拒绝服务(DDoS)攻击。建议的框架通过使用基于VLAN的网络隔离来保护灵巧家庭。该架构有两个子网:一个是未验证的设备,另一个是已验证的设备,两者都由SDN控制器管理。轻量级的无状态流为基础的功能,包括TCP,UDP协议的百分比,数据包计数和大小,IP多样性比率,提出了有效的功能集合。执行进一步的分析以最小化要在灵巧家庭网络中的资源受限边缘设备上运行的训练数据。三种流行的机器学习算法,包括K-近邻,随机森林和支持向量机,用于对物联网设备进行分类并检测不同类型的DDoS攻击,包括TCP-SYN,UDP和DDoS攻击。该系统的有效性和效率通过模拟由Open vSwitch、Faucet SDN控制器和来自两个不同测试床的多个物联网设备跟踪组成的网络进行评估。

英文摘要:

IoT devices have become popular targets for various network attacks due to their lack of industry-wide security standards. In this work, we focus on smart home IoT device identification and defending them against Distributed Denial of Service (DDoS) attacks. The proposed framework protects smart homes by using VLAN-based network isolation. This architecture has two VLANs: one with non-verified devices and the other with verified devices, both of which are managed by the SDN controller. Lightweight stateless flow-based features, including ICMP, TCP, and UDP protocol percentage, packet count and size, and IP diversity ratio, are proposed for efficient feature collections. Further analysis is performed to minimize training data to run on resource-constrained edge devices in smart home networks. Three popular machine learning algorithms, including K-Nearest-Neighbors, Random Forest, and Support Vector Machines, are used to classify IoT devices and detect different types of DDoS attacks, including TCP-SYN, UDP, and ICMP. The system's effectiveness and efficiency are evaluated by emulating a network consisting of an Open vSwitch, Faucet SDN controller, and several IoT device traces from two different testbeds.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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