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Data generation and knowledge sharing for robust intrusion detection in IoT systems
- Citation Author(s):
-
AminKaveh
Uppsala UniversityChristianRohnerUppsala University - Submitted by:
- Christian Rohner
- Last updated:
- Fri, 11/01/2024 - 13:10
- DOI:
- 10.21227/3y2p-4d62
- Data Format:
- Link to Paper:
- License:
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Abstract
The data set includes attack implementations in an Internet of Things (IoT) context. The IoT nodes use Contiki-NG as their operating system and the data is collected from the Cooja simulation environment where a large number of network topologies are created. Blackhole and DIS-flooding attacks are implemented to attack the RPL routing protocol.
The datasets includes log file output from the Cooja simulator and a pre-processed feature set as input to an intrusion detection model.
Instructions:
(tbd, data will be uploaded before November 1, 2024)
Code for pre-processing, modelling and knowledge sharing: https://github.com/uu-core/iot-ids-models
Funding Agency:
Vinnova (Sweden's innovation agency)
Grant Number:
2021-02423; 2023-02982
Dataset Files
data-traces1-NOMS-2023.zip (12.81 GB)
README.txt (1.02 kB)
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