Results 21 to 30 of about 8,328,816 (311)

Adversarial attacks on deep learning models in smart grids

Energy Reports, 2022
A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
doaj   +1 more source

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Sensors, 2023
Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes.
Mohammed Alkhowaiter, Hisham Kholidy, Mnassar A. Alyami, Abdulmajeed Alghamdi, Cliff Zou   +4 more
doaj   +1 more source

A reading survey on adversarial machine learning: Adversarial attacks and their understanding [PDF]

arXiv.org, 2023
Deep Learning has empowered us to train neural networks for complex data with high performance. However, with the growing research, several vulnerabilities in neural networks have been exposed.
Shashank Kotyan
semanticscholar   +1 more source

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, Sensen Guo, Xiaoyu Li, Xiaoyu Li, Zhiying Mu, Zhiying Mu   +5 more
doaj   +1 more source

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning [PDF]

Pattern Recognition, 2017
Deep neural networks and machine-learning algorithms are pervasively used in several applications, ranging from computer vision to computer security. In most of these applications, the learning algorithm has to face intelligent and adaptive attackers who
B. Biggio, F. Roli
semanticscholar   +1 more source

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
doaj   +1 more source

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2021
In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.
Ishai Rosenberg, A. Shabtai, Y. Elovici, L. Rokach   +3 more
semanticscholar   +1 more source

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, Junyi Hu, Yang Liu, Kaushik Roy, Xiaohong Yuan, Jinsheng Xu   +5 more
doaj   +1 more source

Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition

, 2022
This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available ...
Wang, L, Günthermann, L, Philippides, A, Simpson, I, Roggen, D   +4 more
core   +4 more sources

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., Ayoob Mohamed, Poravi Guhanathan   +2 more
doaj   +1 more source