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Adversarial Machine Learning - Industry Perspectives [PDF]

SSRN Electronic Journal, 2020
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems.
Ram Shankar Siva Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, Sharon Xia   +7 more
semanticscholar   +6 more sources

Adversarial machine learning [PDF]

Proceedings of the 4th ACM workshop on Security and artificial intelligence, 2019
In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent.
Ling Huang, A. Joseph, B. Nelson, Benjamin I. P. Rubinstein, J. D. Tygar   +4 more
semanticscholar   +3 more sources

Adversarial Machine Learning:

This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and ...
Alina Oprea
semanticscholar   +5 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

eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

IEEE Access, 2022
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and
Ivan Vaccari, Alberto Carlevaro, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli   +4 more
doaj   +2 more sources

Adversarial Machine Learning in Text Processing: A Literature Survey

IEEE Access, 2022
Machine learning algorithms represent the intelligence that controls many information systems and applications around us. As such, they are targeted by attackers to impact their decisions.
Izzat Alsmadi, Nura Aljaafari, Mahmoud Nazzal, Shadan Alhamed, Ahmad H. Sawalmeh, Conrado P. Vizcarra, Abdallah Khreishah, Muhammad Anan, Abdulelah Algosaibi, Mohammed Abdulaziz Al-Naeem, Adel Aldalbahi, Abdulaziz Al-Humam   +11 more
doaj   +2 more sources

Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review

IEEE Access, 2020
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection ...
Nuno Martins, Jose Magalhaes Cruz, Tiago Cruz, Pedro Henriques Abreu   +3 more
doaj   +2 more sources

Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets

International Conference on Information Systems Security and Privacy, 2021
Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the
Yulexis Pacheco, Weiqing Sun
openalex   +2 more sources

STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection [PDF]

Sensors
Network intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep ...
Juntong Zhu, Zhihao Chen, Rong Cong, Hongyu Sun, Yanhua Dong   +4 more
doaj   +2 more sources

Adversarial Machine Learning in Cybersecurity: Attacks and Defenses

International Journal of Management Science Research
Adversarial Machine Learning (AML) refers to the research field that involves testing and improving machine learning models by introducing adversarial samples or attack techniques.
Ke Hu, Jian Xu, Yong Wang, Heyao Chen, Zepeng Shen   +4 more
openalex   +3 more sources