Results 11 to 20 of about 160,235 (299)

Quantum adversarial machine learning [PDF]

Physical Review Research, 2020
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
doaj   +4 more sources

Adversarial machine learning phases of matter

Quantum Frontiers, 2023
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations:
Si Jiang, Sirui Lu, Dong-Ling Deng
doaj   +3 more sources

Adversarial Machine Learning [PDF]

, 2022
Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine ...
Hernández-Castro, C.J., Liu, Z., Serban, A.C., Tsingenopoulos, I., Joosen, W.   +4 more
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On the Economics of Adversarial Machine Learning [PDF]

IEEE Transactions on Information Forensics and Security
Florian Merkle, Maximilian Samsinger, Pascal Schöttle, Tomáš Pevný   +3 more
openalex   +2 more sources

Adversarial Machine Learning - Industry Perspectives [PDF]

SSRN Electronic Journal, 2020
Minor Typos corrected 7 pages, 1 ...
Ram Shankar Siva Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, Sharon Xia   +7 more
openalex   +4 more sources

Politics of Adversarial Machine Learning [PDF]

SSRN Electronic Journal, 2020
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights.
Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar   +3 more
openaire   +2 more sources

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

IEEE Access, 2020
Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
doaj   +1 more source

Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network

IEEE Access, 2023
Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing.
Zhipeng Liu, Junyi Hu, Yang Liu, Kaushik Roy, Xiaohong Yuan, Jinsheng Xu   +5 more
doaj   +1 more source

Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review

Applied Computer Systems, 2021
In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning.
Mahima K. T. Y., Ayoob Mohamed, Poravi Guhanathan   +2 more
doaj   +1 more source

EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT

Heliyon, 2023
The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li, Jingxuan Wang, Yuhe Wang, Guohui Zhou, Yan Zhao   +4 more
doaj   +1 more source