Results 41 to 50 of about 8,328,816 (311)

On the adversarial robustness of Bayesian machine learning models [PDF]

, 2022
Bayesian machine learning (ML) models have long been advocated as an important tool for safe artificial intelligence. Yet, little is known about their vulnerability against adversarial attacks.
Blaas, Arno
core   +1 more source

Adversarial Attacks and Defenses in Deep Learning

Engineering, 2020
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu   +3 more
doaj   +1 more source

Research on filter-based adversarial feature selection against evasion attacks

Dianxin kexue, 2023
With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are
Qimeng HUANG, Miaomiao WU, Yun LI
doaj   +2 more sources

Adversarial Machine Learning at Scale

CoRR, 2016
17 pages, 5 ...
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio   +2 more
openaire   +3 more sources

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review [PDF]

IEEE Communications Surveys and Tutorials, 2020
Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success ...
D. Adesina, C. Hsieh, Y. Sagduyu, Lijun Qian   +3 more
semanticscholar   +1 more source

Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case [PDF]

International Black Sea Conference on Communications and Networking, 2021
6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems.
Evren Catak, Ferhat Ozgur Catak, A. Moldsvor   +2 more
semanticscholar   +1 more source

A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial Attacks

Advances in Electrical and Computer Engineering, 2023
In recent years, machine learning (ML) has had a significant influence on the discipline of computer security. In network security, intrusion detection systems increasingly employ machine learning techniques.
NATHANIEL, D., SOOSAI, A.
doaj   +1 more source

Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network

Sensors, 2023
Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate
Andrei-Grigore Mari, Daniel Zinca, Virgil Dobrota   +2 more
doaj   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

Applied Sciences, 2021
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang, Jiahui Xu, Wanpeng Zhang, Weidong Wang   +3 more
doaj   +1 more source

Adversarial machine learning: a review of methods, tools, and critical industry sectors

Artificial Intelligence Review
The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has produced high-performance models widely used in various applications, ranging from image recognition and chatbots to autonomous driving ...
Sotiris Pelekis, Thanos Koutroubas, Afroditi Blika, Anastasis Berdelis, Evangelos Karakolis, Christos Ntanos, Evangelos Spiliotis, Dimitris Askounis   +7 more
semanticscholar   +1 more source