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

Ran Yahalom, David Barishev, Alon Steren, Yonatan Nameri, Maxim Roytman, Angel Porgador, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

Abstract

The datasets in this article are produced to evaluate the ability of MIL-STD-1553 intrusion detection systems to detect attacks that emulate normal non-periodical messages, at differing attack occurrence rates. And different data representations. We present three streams of simulated MIL-STD-1553 traffic containing both normal and attack messages corresponding to packets that were injected into the bus by a malicious remote terminal. The implemented attacks emulate normal non-periodical communication so detecting them with a low false positive rate is non-trivial. Each stream is separated into a training set of normal messages and a test set of both normal and attack messages. The test sets differ by the occurrence rate of attack messages (0.01%, 0.10%, and 1.00%). Each stream is also preprocessed into a dataset of message sequences so that it can be used for sequential anomaly detection analysis. The sequential test sets differ by the occurrence rate of attack sequences (0.14%, 1.26%, and 11.01%). All dataset files can be found in Mendeley Data, doi:10.17632/jvgdrmjvs3.3.

Original languageAmerican English
Article number103863
JournalData in Brief
Volume23
DOIs
StatePublished - 1 Apr 2019

All Science Journal Classification (ASJC) codes

  • General

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