Publication:
Robustness and Vulnerability Measures of Deep Learning Methods for Cyber Defense

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Authors
Martinsen, Thor
Kang, Wei
Subjects
Deep Learning
Machine Learning
Cybersecurity
Advisors
Date of Issue
2022
Date
2022
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Navy networks and infrastructures are under frequent cyberattack. One developing area of application of Artificial Intelligence (AI) and Machine Learning (ML) is cybersecurity. However, some weakness of machine learning, such as the lack of interpretability and the susceptibility to adversarial data, are important issues that must be studied for reliable and safe applications of AI tools. The robustness of deep learning (DL) techniques used in computer vision and language processing have been extensively studied. However, less is currently known about the vulnerabilities and robustness of DL methods suitable in cybersecurity applications. The goal of this research is to investigate mathematical concepts and quantitative measures of robustness and vulnerability to adversarial data for cybersecurity DL and to create computational algorithms capable of quantitatively evaluating the robustness and vulnerability of DL tools. The tasks of the project include literature review, an innovative study of mathematical concepts, the development of computational algorithms, the validation of the concepts and algorithms through examples. The deliverables of the project include technical reports, student thesis, and technical papers for publication. This work will enhance understanding of vulnerabilities of deep learning systems that could be incorporated in future DoN networks, and provide the US Navy with computational tools capable of measuring the robustness of the AI enabled systems.
Type
Technical Report
Description
NPS NRP Technical Report
Department
Applied Mathematics (MA)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
PS-MA-23-001
Sponsors
Navy Cyber Defense Operations Command
N2/N6 - Information Warfare
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited. 
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.