Datasets of RT spoofing attacks on MIL-STD-1553 communication traffic

Area of Science:

• Cybersecurity
• Aerospace Engineering
• Data Science

Background:

• MIL-STD-1553 buses are critical in aerospace systems.
• Intrusion detection systems (IDS) are vital for securing these buses.
• Detecting attacks mimicking normal non-periodical messages is challenging.

Purpose of the Study:

• To evaluate the effectiveness of MIL-STD-1553 intrusion detection systems.
• To assess detection capabilities against attacks emulating normal non-periodical messages.
• To provide datasets for analyzing detection performance at different attack rates and data representations.

Main Methods:

• Simulated three streams of MIL-STD-1553 traffic with normal and attack messages.
• Injected attack messages emulating normal non-periodical communication.
• Separated data into training (normal) and testing (normal + attack) sets.
• Preprocessed message sequences for sequential anomaly detection analysis.

Main Results:

• Attack message occurrence rates in test sets: 0.01%, 0.10%, 1.00%.
• Attack sequence occurrence rates in sequential test sets: 0.14%, 1.26%, 11.01%.
• Datasets available for download to facilitate further research.

Conclusions:

• The study provides valuable datasets for evaluating MIL-STD-1553 IDS performance.
• Understanding detection capabilities against subtle attacks is crucial for system security.
• Further research can utilize these datasets to improve anomaly detection algorithms.