  The proliferation of IoT systems, has seen them targeted by malicious third
parties. To address this, realistic protection and investigation
countermeasures need to be developed. Such countermeasures include network
intrusion detection and network forensic systems. For that purpose, a
well-structured and representative dataset is paramount for training and
validating the credibility of the systems. Although there are several network,
in most cases, not much information is given about the Botnet scenarios that
were used. This paper, proposes a new dataset, 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 existing datasets.
This work provides the baseline for allowing botnet identificaiton across
IoT-specifc networks. The Bot-IoT dataset can be accessed at [1].
