Serial Number: 77
Year: 2020
Kind of Traffic: Real
Publicly Available: Yes
Count of Records: 10k:pkts, 67k: flows
Features Count:
No. of citations: 19
Attack Type: duplication, interception, etc
Download Links: https://www.ce.cit.tum.de/mmk/dad/
Abstract: Vigoya and colleagues generated the publicly available DAD (Dataset for Anomaly Detection), comprising annotated traffic data of network of IoT. The objective was to create an extensive dataset with diverse scenarios and annotations that could be employed by machine learning algorithms to identify anomalies in sensor networks of IoT. DAD dataset was obtained from a simulated virtual environment mimicking the behaviour of sixteen IoT temperature sensors deployed in a data center. Using MQTT over IP, After every five minutes these sensors sent measurement samples. Network traffic was captured over a period of seven days at the MQTT broker. To introduce attack scenarios, alterations were made to the sensors on five recorded days, employing three different methods: Interception (withholding certain temperature measurements), Modification (altering temperature measurements), and Duplication (sending more packets than usual). 101,583 packets are in complete dataset each appropriately labeled as normal or anomalous. MQTT packets constitute 63.3% of the dataset, with 16% of them identified as malicious.