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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 +8 more sources
Future Internet, 2023 Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks ...
Afnan Alotaibi, Murad A. Rassam
doaj +4 more sources
Journal of Cybersecurity and Privacy, 2022 Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection.
Andrew McCarthy +3 more
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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 +11 more
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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 +4 more
semanticscholar +3 more sources
Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model. [PDF]
Sensors (Basel)Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs).
Alhoraibi L, Alghazzawi D, Alhebshi R.
europepmc +2 more sources
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 +7 more
semanticscholar +7 more sources
Adversarial Machine Learning [PDF]
IEEE Internet Computing, 2011 The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near ...
J D Tygar
exaly +3 more sources
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 ...
Apostol T. Vassilev
semanticscholar +7 more sources
A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning. [PDF]
IEEE Trans Emerg Top Comput Intell, 2020 Machine Learning (ML) algorithms, specifically supervised learning, are widely used in modern real-world applications, which utilize Computational Intelligence (CI) as their core technology, such as autonomous vehicles, assistive robots, and biometric ...
Sadeghi K, Banerjee A, Gupta SKS.
europepmc +2 more sources