Abstract
The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.
Original language | English (US) |
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Title of host publication | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
Publisher | Association for Computing Machinery |
Pages | 2137-2146 |
Number of pages | 10 |
Volume | 2015-August |
ISBN (Print) | 9781450336642 |
DOIs | |
State | Published - Aug 10 2015 |
Event | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia Duration: Aug 10 2015 → Aug 13 2015 |
Other
Other | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 |
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Country/Territory | Australia |
City | Sydney |
Period | 8/10/15 → 8/13/15 |
Keywords
- Causality
- Rule learning
- Security informatics
ASJC Scopus subject areas
- Software
- Information Systems