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Adversarial Machine Learning in the Wild
2021Deep neural networks are making their way into our everyday lives at an increasing rate. While the adoption of these models has greatly improved our everyday lives, it has also opened the door to new vulnerabilities in real-world systems. More specifically, in the scope of this work we are interested in one class of vulnerabilities: adversarial attacks.
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An Introduction to Adversarial Machine Learning
2017Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine ...
Atul Kumar 0002 +2 more
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The Many Faces of Adversarial Machine Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision ...
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Enablers of Adversarial Attacks in Machine Learning
MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018The proliferation of machine learning (ML) and artificial intelligence (AI) systems for military and security applications creates substantial challenges for designing and deploying such mechanisms that would learn, adapt, reason and act with Dinky, Dirty, Dynamic, Deceptive, Distributed (D5) data. While Dinky and Dirty challenges have been extensively
Rauf Izmailov +4 more
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Adversarial machine learning for spam filters
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020Email spam filters based on machine learning techniques are widely deployed in today's organizations. As our society relies more on artificial intelligence (AI), the security of AI, especially the machine learning algorithms, becomes increasingly important and remains largely untested. Adversarial machine learning, on the other hand, attempts to defeat
Bhargav Kuchipudi +2 more
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Machine Learning in Adversarial RF Environments
IEEE Communications Magazine, 2019With more and more autonomous deployments of wireless networks, accurate knowledge of the RF environment is becoming indispensable. Various techniques have been developed over the years that can not only assess the RF environment but can also characterize the various radio transmitters (sources) that define the ambient RF environment.
Debashri Roy +2 more
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Adversarial Machine Learning in Recommender Systems
ACM SIGIR Forum, 2023Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for instance, we select the news to read, the product to buy, the friend to connect with, and the movie to watch. While enormous academic research efforts have been mainly focused on getting high-quality recommendations to reach maximum user satisfaction, little ...
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Adversarial Machine Learning: A Literature Review
2018Machine learning is becoming more and more utilized as a tool for businesses and governments to aid in decision making and automation processes. These systems are also susceptible to attacks by an adversary, who may try evading or corrupting the system.
Sam Thomas, Nasseh Tabrizi
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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
ACM Computing Surveys, 2022Ishai Rosenberg +2 more
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