Metrics
4 Record Views
Book chapter
Trinity: Trust, Resilience and Interpretability of Machine Learning Models
Game Theory and Machine Learning for Cyber Security, pp.317-333
John Wiley & Sons, Inc
09/22/2021
Abstract
Despite the remarkable strides over the last decade in the performance of machine learning techniques, their applications are typically limited to nonadversarial benign environments. The use of deep learning in applications such as biometric recognition, and intrusion detection, require them to operate in adversarial environments. But the overwhelming empirical studies and theoretical results have shown that these methods are extremely fragile and susceptible to adversarial attacks. The rationale for why these methods make the decisions they do are also notoriously difficult to interpret; understanding such rationale may be crucial for the aforementioned applications. In this chapter, we discuss the connections between these related challenges, and describe a novel integrated approach, Trinity (
T
rust,
R
esilience and
IN
terpretabil
ITY
), for analyzing these models.
Related links
Details
- Title
- Trinity: Trust, Resilience and Interpretability of Machine Learning Models
- Publication Details
- Game Theory and Machine Learning for Cyber Security, pp.317-333
- Resource Type
- Book chapter
- Publisher
- John Wiley & Sons, Inc; Hoboken, NJ, USA
- Number of pages
- 17
- Copyright
- © 2021 John Wiley & Sons, Inc.
- Identifiers
- 99381512042606600
- Academic Unit
- Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English