TY - JOUR
T1 - Trailblazing the Artificial Intelligence for Cybersecurity Discipline
T2 - A Multi-Disciplinary Research Roadmap
AU - Samtani, Sagar
AU - Kantarcioglu, Murat
AU - Chen, Hsinchun
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Numbers OAC-1917117 (CICI), CNS-1936370 (SaTC CORE), CNS-1850362 (CRII SaTC), and DGE-2038483 (SaTC-EDU). Authors’ addresses: S. Samtani (corresponding author), Department of Operations and Decision Technologies, Indiana University, 1275 E. 10th St., Bloomington, Indiana 47405; email: [email protected]; M. Kantarcioglu, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080; email: [email protected]; H. Chen, Department of Management Information Systems, University of Arizona, 1130 E. Helen St., McClelland Hall 430, Tucson, AZ 85721; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2020 Association for Computing Machinery. 2158-656X/2020/12-ART17 $15.00 https://doi.org/10.1145/3430360
Funding Information:
Table 3. Summary of Selected National Science Foundation (NSF) Funding Opportunities to Support AI for Cybersecurity Research and Education Programs
Publisher Copyright:
© 2020 ACM.
PY - 2020/12
Y1 - 2020/12
N2 - Cybersecurity has rapidly emerged as a grand societal challenge of the 21st century. Innovative solutions to proactively tackle emerging cybersecurity challenges are essential to ensuring a safe and secure society. Artificial Intelligence (AI) has rapidly emerged as a viable approach for sifting through terabytes of heterogeneous cybersecurity data to execute fundamental cybersecurity tasks, such as asset prioritization, control allocation, vulnerability management, and threat detection, with unprecedented efficiency and effectiveness. Despite its initial promise, AI and cybersecurity have been traditionally siloed disciplines that relied on disparate knowledge and methodologies. Consequently, the AI for Cybersecurity discipline is in its nascency. In this article, we aim to provide an important step to progress the AI for Cybersecurity discipline. We first provide an overview of prevailing cybersecurity data, summarize extant AI for Cybersecurity application areas, and identify key limitations in the prevailing landscape. Based on these key issues, we offer a multi-disciplinary AI for Cybersecurity roadmap that centers on major themes such as cybersecurity applications and data, advanced AI methodologies for cybersecurity, and AI-enabled decision making. To help scholars and practitioners make significant headway in tackling these grand AI for Cybersecurity issues, we summarize promising funding mechanisms from the National Science Foundation (NSF) that can support long-term, systematic research programs. We conclude this article with an introduction of the articles included in this special issue.
AB - Cybersecurity has rapidly emerged as a grand societal challenge of the 21st century. Innovative solutions to proactively tackle emerging cybersecurity challenges are essential to ensuring a safe and secure society. Artificial Intelligence (AI) has rapidly emerged as a viable approach for sifting through terabytes of heterogeneous cybersecurity data to execute fundamental cybersecurity tasks, such as asset prioritization, control allocation, vulnerability management, and threat detection, with unprecedented efficiency and effectiveness. Despite its initial promise, AI and cybersecurity have been traditionally siloed disciplines that relied on disparate knowledge and methodologies. Consequently, the AI for Cybersecurity discipline is in its nascency. In this article, we aim to provide an important step to progress the AI for Cybersecurity discipline. We first provide an overview of prevailing cybersecurity data, summarize extant AI for Cybersecurity application areas, and identify key limitations in the prevailing landscape. Based on these key issues, we offer a multi-disciplinary AI for Cybersecurity roadmap that centers on major themes such as cybersecurity applications and data, advanced AI methodologies for cybersecurity, and AI-enabled decision making. To help scholars and practitioners make significant headway in tackling these grand AI for Cybersecurity issues, we summarize promising funding mechanisms from the National Science Foundation (NSF) that can support long-term, systematic research programs. We conclude this article with an introduction of the articles included in this special issue.
KW - Cybersecurity
KW - adversarial machine learning
KW - analytics
KW - artificial intelligence
KW - cyber threat intelligence
KW - disinformation
KW - security operations centers
UR - http://www.scopus.com/inward/record.url?scp=85097331327&partnerID=8YFLogxK
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U2 - 10.1145/3430360
DO - 10.1145/3430360
M3 - Article
AN - SCOPUS:85097331327
SN - 2158-656X
VL - 11
JO - ACM Transactions on Management Information Systems
JF - ACM Transactions on Management Information Systems
IS - 4
M1 - 17
ER -