论文标题
部分可观测时空混沌系统的无模型预测
Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things
论文作者
论文摘要
在发生时检测异常在建筑物和房屋等环境中至关重要，以识别潜在的网络攻击。本文讨论了在发生异常时的各种机制。在建立机器学习模型时，我们阐明了至关重要的考虑。我们从具有不同的原位传感器的多个自我构建（DIY）IoT设备中构建并收集了数据，并找到了有效的方法来找到观点，上下文和结合异常。当处理以不同的采样率以及如何在机器学习模型中进行处理时，我们还讨论了几个挑战和潜在解决方案。本文还研究了根据环境条件提取子数据集的利弊。
Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.
