Results 11 to 20 of about 156,834 (158)
Adversarial Machine Learning on Social Network: A Survey
Frontiers in Physics, 2021 In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation.
Sensen Guo +5 more
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ACM SIGKDD Explorations Newsletter, 2021 Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations.
Delobelle, Pieter +5 more
openaire +6 more sources
A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
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Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
IEEE Access, 2023 Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing.
Zhipeng Liu +5 more
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Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review
Applied Computer Systems, 2021 In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning.
Mahima K. T. Y. +2 more
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EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
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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
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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning [PDF]
, 2018 Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity.
Biggio, Battista, Roli, Fabio
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Impact of adversarial examples on deep learning models for biomedical image segmentation [PDF]
, 2019 Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples.
C Pena-Betancor +3 more
core +4 more sources
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 +3 more
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