Results 1 to 10 of about 33,606 (255)

Adversarial Machine Learning Attacks against Intrusion Detection Systems: A Survey on Strategies and Defense

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
doaj   +3 more sources

Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey

Journal of Cybersecurity and Privacy, 2022
Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection.
Andrew McCarthy, Essam Ghadafi, Panagiotis Andriotis, Phil Legg   +3 more
doaj   +3 more sources

Adversarial Machine Learning in Text Processing: A Literature Survey

IEEE Access, 2022
Machine learning algorithms represent the intelligence that controls many information systems and applications around us. As such, they are targeted by attackers to impact their decisions.
Izzat Alsmadi, Nura Aljaafari, Mahmoud Nazzal, Shadan Alhamed, Ahmad H. Sawalmeh, Conrado P. Vizcarra, Abdallah Khreishah, Muhammad Anan, Abdulelah Algosaibi, Mohammed Abdulaziz Al-Naeem, Adel Aldalbahi, Abdulaziz Al-Humam   +11 more
doaj   +3 more sources

STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection [PDF]

Sensors
Network intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep ...
Juntong Zhu, Zhihao Chen, Rong Cong, Hongyu Sun, Yanhua Dong   +4 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, Daniyal Alghazzawi, Reem Alotaibi, Osama Rabie   +3 more
doaj   +2 more sources

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

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

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