Adversarial Machine Learning based Partial-model Attack in IoT [PDF]
WiseML@WiSec, 2020 As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications.
Zhengping Luo +4 more
openalex +3 more sources
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
IEEE Access, 2020 Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection ...
Nuno Martins +3 more
doaj +2 more sources
Adversarial attacks against supervised machine learning based network intrusion detection systems.
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
doaj +2 more sources
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 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 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
doaj +1 more source
Adversarial Machine Learning Attacks on Multiclass Classification of IoT Network Traffic
ARES, 2023 Machine Learning-based Intrusion Detection Systems have been proven to be very effective in the protection of IoT Networks. However, the expansion of Adversarial Machine Learning attacks threatens their efficacy affecting also the security of IoT ...
Vasileios Pantelakis +3 more
semanticscholar +1 more source
Adversarial Machine Learning-Industry Perspectives [PDF]
2020 IEEE Security and Privacy Workshops (SPW), 2020 Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems.
R. Kumar +7 more
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
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
doaj +1 more source