论文标题
部分可观测时空混沌系统的无模型预测
Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devices
论文作者
论文摘要
物联网设备及其用于监视机器和设备运行的使用量增加，可以增加对在设备上运行的异常检测算法的兴趣。但是，困难是设备上可用的计算和内存资源的局限性。对于微控制器（MCUS），这些是程序的单一兆字节和数百千键的工作记忆。因此，必须将算法与设备的功能适当匹配。在本文中，我们分析了MCU上局部Outliner因子（LOF）算法的异常检测和实施的处理管道。我们还表明，可以直接在设备上训练这样的算法，从而在真实设备中使用该解决方案具有很大的潜力。
The growing number of IoT devices and their use to monitor the operation of machines and equipment increases interest in anomaly detection algorithms running on devices. However, the difficulty is the limitations of the available computational and memory resources on the devices. In the case of microcontrollers (MCUs), these are single megabytes of program and several hundred kilobytes of working memory. Consequently, algorithms must be appropriately matched to the capabilities of the devices. In the paper, we analyse the processing pipeline for anomaly detection and implementation of the Local Outliner Factor (LOF) algorithm on a MCU. We also show that it is possible to train such an algorithm directly on the device, which gives great potential to use the solution in real devices.
