Results 21 to 30 of about 6,909 (116)

Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT

open access: yesIEEE Access
Machine learning-based Distributed Denial of Service (DDoS) attack detection systems have proven effective in detecting and preventing DDoD attacks in Internet of Things (IoT) systems.
Yonas Kibret Beshah   +2 more
doaj   +1 more source

Comprehensive comparisons of gradient-based multi-label adversarial attacks

open access: yesComplex & Intelligent Systems
Adversarial examples which mislead deep neural networks by adding well-crafted perturbations have become a major threat to classification models. Gradient-based white-box attack algorithms have been widely used to generate adversarial examples.
Zhijian Chen   +4 more
doaj   +1 more source

DTFA: Adversarial attack with discrete cosine transform noise and target features on deep neural networks

open access: yesIET Image Processing, 2023
Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment.
Dong Yang, Wei Chen, Songjie Wei
doaj   +1 more source

Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning

open access: yesApplied Sciences, 2022
The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is
Kudzai Sauka   +3 more
doaj   +1 more source

Adversarial Attacks to Manipulate Target Localization of Object Detector

open access: yesIEEE Access
Adversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu   +7 more
doaj   +1 more source

AB-VIP: Attack Method Using Brightness Information for Visibility Improvement of Adversarial Perturbations on the NMS Algorithm

open access: yesIEEE Access
As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS ...
Gwang-Nam Kim   +4 more
doaj   +1 more source

A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

open access: yesIEEE Access, 2020
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu   +4 more
doaj   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

open access: yesApplied 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   +3 more
doaj   +1 more source

Multi-Level Chinese Adversarial Example Generation Method Based on Glyph and Semantic [PDF]

open access: yesJisuanji gongcheng
Deep neural network language models are vulnerable to adversarial example attacks during application. To address this issue, adversarial examples are typically generated by adding minor perturbations to the original samples to mislead the model into ...
SUN Yu, WANG Hongjie, DU Yanhui, LIU Nan
doaj   +1 more source

Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems

open access: yesMathematics
Adversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities.
Mun-Suk Kim
doaj   +1 more source

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