N-BaIoT processed for anomaly detection

The original data comes from the work of Meidan et al. [1]. It was preprocessed in this setting for comparative analysis of anomaly detection. The following steps have been taken as preprocessing: (1) five devices have been selected: Danmini doorbell, Ecobee thermostat, Philips baby monitor, Provision security camera, Samsung webcam, (2) for each botnet, the malicious traffic of all five behaviour types have been merged, (3) for each device and botnet combination, malicious requests have been sampled to comprise 5% of the final dataset. [1] Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12-22.原始数据集源自Meidan等人[1]的研究工作。本研究在此基础上开展预处理工作，以支撑异常检测的对比分析。预处理流程如下：(1) 选取5款设备：Danmini门铃、Ecobee智能恒温器、Philips婴儿监护仪、Provision安防摄像头、Samsung网络摄像头；(2) 针对每类僵尸网络（botnet），将全部5种行为类型的恶意流量予以合并；(3) 针对每款设备与僵尸网络的组合，对恶意请求进行采样，使其占最终数据集的5%。[1] Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-baiot——基于网络的物联网（Internet of Things, IoT）僵尸网络攻击检测：深度自编码器的应用. IEEE普适计算, 17(3), 12-22.

搜集汇总

背景与挑战

背景概述

该数据集是N-BaIoT数据的预处理版本，专为异常检测比较分析设计。它包含五种物联网设备（如智能门铃、恒温器等）的恶意流量数据，恶意请求经采样后占数据总量的5%，适用于物联网安全研究。