Results 61 to 70 of about 1,143,792 (206)

Multitask adversarial attack with dispersion amplification

EURASIP Journal on Information Security, 2021
Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited.
Pavlo Haleta, Dmytro Likhomanov, Oleksandra Sokol   +2 more
doaj   +1 more source

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning [PDF]

, 2023
Prathyusha Devabhakthini, Sasmita Parida, Raj Mani Shukla, Suvendu Chandan Nayak   +3 more
openalex   +1 more source

Classical autoencoder distillation of quantum adversarial manipulations

Physical Review Research
Quantum neural networks have been proven robust against classical adversarial attacks, but their vulnerability against quantum adversarial attacks is still a challenging problem.
Amena Khatun, Muhammad Usman
doaj   +1 more source

Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

IEEE Access
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex ...
Sabrine Ennaji, Fabio de Gaspari, Dorjan Hitaj, Alicia Kbidi, Luigi Vincenzo Mancini   +4 more
doaj   +1 more source

Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model [PDF]

, 2022
Olivér M. Balogh, Bettina Benczik, András Horváth, Mátyás Pétervári, Péter Csermely, Péter Ferdinandy, Bence Ágg   +6 more
openalex   +1 more source

Learning atomic forces from uncertainty-calibrated adversarial attacks

npj Computational Materials
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs).
Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore   +5 more
doaj   +1 more source

Adversarial Halftone QR Code

IEEE Access
Recent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing, Karin Sumongkayothin, Prarinya Siritanawan, Kazunori Kotani   +3 more
doaj   +1 more source

A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection [PDF]

, 2023
Marta Catillo, Andrea Del Vecchio, Antonio Pecchia, Umberto Villano   +3 more
openalex   +1 more source

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

IEEE Open Journal of the Industrial Electronics Society
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to ...
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov   +4 more
doaj   +1 more source

Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks [PDF]

, 2021
Matthew Ciolino, Josh Kalin, David Noever   +2 more
openalex   +1 more source