The Bot-IoT dataset

The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used. This paper proposes a new dataset, so-called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the benchmark datasets. This work provides the baseline for allowing botnet identification across IoT-specific networks.

物联网（IoT）系统的日益普及，使其成为恶意第三方的攻击目标。为应对这一挑战，亟需有效研发网络入侵检测、网络取证系统等切实可行的防护与调查对抗措施。为此，结构完善且具有代表性的数据集，对于训练与验证这类系统的可靠性至关重要。尽管现有多款网络数据集，但多数都未充分披露其所采用的僵尸网络（Botnet）场景相关信息。本文提出一款名为Bot-IoT的新型数据集，该数据集涵盖合法物联网网络流量、模拟物联网网络流量，以及多种类型的攻击流量。同时，针对现有数据集在完整网络信息采集、精准标签标注以及覆盖新型复杂攻击多样性方面的缺陷，本文还搭建了贴近真实场景的测试床环境。最后，本文以基准数据集为对照，采用多种统计与机器学习方法，从取证角度评估了Bot-IoT数据集的可靠性。本研究为物联网专属网络中的僵尸网络识别任务提供了基准参照。

搜集汇总

背景与挑战

背景概述

The Bot-IoT dataset是一个包含正常和恶意IoT网络流量的数据集，专门设计用于网络入侵检测和取证系统的训练与验证。数据集涵盖了多种攻击类型，如DDoS、DoS等，并以CSV格式提供，便于机器学习算法的应用。