Active Defense Against Social Engineering: The Case for Human Language Technology

Adam Dalton, Ehsan Aghaei, Ehab Al-Shaer, Archna Bhatia, Esteban Castillo, Zhuo Cheng, Sreekar Dhaduvai, Qi Duan, Bryanna Hebenstreit, Md Mazharul Islam, Younes Karimi, Amir Masoumzadeh, Brodie Mather, Sashank Santhanam, Samira Shaikh, Alan Zemel, Tomek Strzalkowski, Bonnie J. Dorr


Abstract
We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker’s time and resources.
Anthology ID:
2020.stoc-1.1
Volume:
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | STOC | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1–8
URL:
https://www.aclweb.org/anthology/2020.stoc-1.1
DOI:
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.stoc-1.1.pdf

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