Results 251 to 260 of about 5,384,791 (287)

Detecting Adversarial Examples Using Data Manifolds

MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018
Models produced by machine learning, particularly deep neural networks, are state-of-the-art for many machine learning tasks and demonstrate very high prediction accuracy. Unfortunately, these models are also very brittle and vulnerable to specially crafted adversarial examples.
Susmit Jha, Uyeong Jang, Somesh Jha, Brian Jalaian   +3 more
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Detecting adversarial examples of text

2021
Although deep neural networks have achieved state-of-the-art performance in various tasks, a lot of their decisions are non-interpretable because high dimensional feature vectors cause deep neural net functions to be extremely difficult to visualize, widely used gradient descent is an approximate solution, not an analytical solution, etc.
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Adversarial DGA Domain Examples Generation and Detection

2020 International Conference on Control, Robotics and Intelligent System, 2020
Botnets have long relied on the Domain Generation Algorithm (DGA) to survive to this day. The detection rate of the DGA detection methods based on machine learning is already high. However, the models trained by the existing data sets sometimes are blind to new variant domains.To mitigate such problem, a method based on generation adversarial networks (
Heng Cao, Chundong Wang 0002, Long Huang, Xiaochun Cheng, Haoran Fu   +4 more
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A Deeper Analysis of Adversarial Examples in Intrusion Detection

2021
During the last decade, machine learning algorithms have massively integrated the defense arsenal made available to security professionals, especially for intrusion detection. However, and despite the progress made in this area, machine learning models have been found to be vulnerable to slightly modified data samples called adversarial examples ...
Mohamed Amine Merzouk, Frédéric Cuppens, Nora Boulahia-Cuppens, Reda Yaich   +3 more
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Towards robust classification detection for adversarial examples

2020 15th International Conference for Internet Technology and Secured Transactions (ICITST), 2020
In the field of computer vision, machine learning (ML) models have been widely used in various tasks to achieve better performance. ML models, however, do a poor job of identifying malicious inputs such as adversarial examples. Abuse adversarial examples can cause security threats in ML-based products or applications.
Huangxiaolie Liu, Dong Zhang, Huijun Chen   +2 more
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Detecting Adversarial Examples Through Image Transformation

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Deep Neural Networks (DNNs) have demonstrated remarkable performance in a diverse range of applications. Along with the prevalence of deep learning, it has been revealed that DNNs are vulnerable to attacks. By deliberately crafting adversarial examples, an adversary can manipulate a DNN to generate incorrect outputs, which may lead ...
Shixin Tian, Guolei Yang, Ying Cai 0001
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Intrusion detection systems vulnerability on adversarial examples

2018 Innovations in Intelligent Systems and Applications (INISTA), 2018
Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system.
Arkadiusz Warzynski, Grzegorz Kolaczek
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A Unified Framework for Detecting Audio Adversarial Examples

Proceedings of the 28th ACM International Conference on Multimedia, 2020
Adversarial attacks have been widely recognized as the security vulnerability of deep neural networks, especially in deep automatic speech recognition (ASR) systems. The advanced detection methods against adversarial attacks mainly focus on pre-processing the input audio to alleviate the threat of adversarial noise.
Xia Du, Chi-Man Pun, Zheng Zhang 0006
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Feature autoencoder for detecting adversarial examples

International Journal of Intelligent Systems, 2022
Hongwei Ye, Xiaozhang Liu
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DeepSHAP Summary for Adversarial Example Detection

2023 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest), 2023
Yi-Ching Lin, Fang Yu 0001
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