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When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium)

Authors:

Tegjyot Singh Sethi,

Mehmed KantardzicAuthors Info & Claims

Ubiquity, Volume 2018, Issue May

Article No.: 1, Pages 1 - 14

https://doi.org/10.1145/3158346

Published: 17 May 2018 Publication History

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Abstract

While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.

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Cited By

Nadeem AVerwer SYang S(2023)Learning About the AdversaryAutonomous Intelligent Cyber Defense Agent (AICA)10.1007/978-3-031-29269-9_6(105-132)Online publication date: 3-Jun-2023
https://doi.org/10.1007/978-3-031-29269-9_6
Thomas SKoleini FTabrizi N(2022)Dynamic defenses and the transferability of adversarial examples2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)10.1109/TPS-ISA56441.2022.00041(276-284)Online publication date: Dec-2022
https://doi.org/10.1109/TPS-ISA56441.2022.00041
Pimsarn CBoongoen TIam-On NNaik NYang L(2022)Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problemComplex & Intelligent Systems10.1007/s40747-022-00739-08:6(4863-4880)Online publication date: 25-Apr-2022
https://doi.org/10.1007/s40747-022-00739-0
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When Good Machine Learning Leads to Bad Security: Big Data (Ubiquity symposium)
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Published In

cover image Ubiquity

Ubiquity Volume 2018, Issue May

May 2018

14 pages

EISSN:1530-2180

DOI:10.1145/3225056

Issue’s Table of Contents

Copyright © 2018 ACM.

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2018

Published in UBIQUITY Volume 2018, Issue May

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Cited By

Nadeem AVerwer SYang S(2023)Learning About the AdversaryAutonomous Intelligent Cyber Defense Agent (AICA)10.1007/978-3-031-29269-9_6(105-132)Online publication date: 3-Jun-2023
https://doi.org/10.1007/978-3-031-29269-9_6
Thomas SKoleini FTabrizi N(2022)Dynamic defenses and the transferability of adversarial examples2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)10.1109/TPS-ISA56441.2022.00041(276-284)Online publication date: Dec-2022
https://doi.org/10.1109/TPS-ISA56441.2022.00041
Pimsarn CBoongoen TIam-On NNaik NYang L(2022)Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problemComplex & Intelligent Systems10.1007/s40747-022-00739-08:6(4863-4880)Online publication date: 25-Apr-2022
https://doi.org/10.1007/s40747-022-00739-0
Gupta MBatra G(2021)Investigation of Machine Learning Assistance to Education2021 5th International Conference on Computing Methodologies and Communication (ICCMC)10.1109/ICCMC51019.2021.9418364(777-782)Online publication date: 8-Apr-2021
https://doi.org/10.1109/ICCMC51019.2021.9418364
Queiroz AKeegan BMckeever S(2020)Moving Targets: Addressing Concept Drift in Supervised Models for Hacker Communication Detection2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)10.1109/CyberSecurity49315.2020.9138894(1-7)Online publication date: Jun-2020
https://doi.org/10.1109/CyberSecurity49315.2020.9138894

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