Adversarial Machine Learning [PDF]
, 2022 Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine ...
Aneesh Sreevallabh Chivukula +4 more
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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
Optimized CatBoost machine learning (OCML) for DDoS detection in cloud virtual machines with time-series and adversarial robustness. [PDF]
Sci RepSamy H, Bahaa-Eldin AM, Sobh MA, Taha A.
europepmc +3 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
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 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 +3 more
semanticscholar +1 more source
ACM SIGKDD Explorations Newsletter, 2021 Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations.
Delobelle, Pieter +5 more
openaire +6 more sources
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 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
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