Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals-猫眼课题宝

DOI：

10.1016/j.procs.2022.10.119

发表时间：

2022-01

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Chenxu Jiang;Chenglong Fu;Zhenyu Zhao;Xiaojiang Du;Yuede Ji

智能家居物联网系统和设备容易受到攻击和故障。因此，随着智能家居部署的普及，用户对其安全和安全问题的担忧也随之出现。在智能家居中，由于网络攻击、设备故障或人为错误，可能会发生各种异常（如火灾或洪水）。这些问题促使研究人员提出了各种异常检测方法。现有的智能家居异常检测工作侧重于检查物联网设备事件的顺序，而忽略了事件的时间信息。这一限制阻止了它们检测导致延迟的异常，而不是丢失/注入事件。为了填补这一空白，本文提出了一种考虑事件间间隔的异常检测方法。我们提出了一种创新的度量来量化两个事件序列之间的时间相似性。我们设计了一种机制来学习常见日常活动事件序列的时间模式。通过将序列与学习到的模式进行比较，可以检测到延迟引起的异常。我们从真实世界的测试平台收集设备事件进行培训和测试。实验结果表明，我们提出的方法对三个日常活动的准确率分别为93%、88%和89%。

Smart home IoT systems and devices are susceptible to attacks and malfunctions. As a result, users’ concerns about their security and safety issues arise along with the prevalence of smart home deployments. In a smart home, various anomalies (such as fire or flooding) could happen due to cyber attacks, device malfunctions, or human mistakes. These concerns motivate researchers to propose various anomaly detection approaches. Existing works on smart home anomaly detection focus on checking the sequence of IoT devices’ events but leave out the temporal information of events. This limitation prevents them from detecting anomalies that cause delay rather than missing/injecting events. To fill this gap, in this paper, we propose a novel anomaly detection method that takes the inter-event intervals into consideration. We propose an innovative metric to quantify the temporal similarity between two event sequences. We design a mechanism for learning the temporal patterns of event sequences of common daily activities. Delay-caused anomalies are detected by comparing the sequence with the learned patterns. We collect device events from a real-world testbed for training and testing. The experiment results show that our proposed method achieves accuracies of 93%, 88%, and 89% for three daily activities.

Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals

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