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 ...
Hernández-Castro, C.J. +4 more
core +5 more sources
Defenses in Adversarial Machine Learning: A Survey
CoRR, 2023 Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases ...
Baoyuan Wu +9 more
semanticscholar +4 more sources
Active machine learning approach to adversarial training improves trade-off between natural accuracy and adversarial robustness [PDF]
Scientific ReportsUnderstanding our world which is open and diverse requires foundation models that generalize well while trustworthy. Adversarial training has been considered to be one of the most effective strategies to achieve robust learning systems, yet adversarial ...
Seyed Mohammad Hadi Mirsadeghi
doaj +2 more sources
eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics
IEEE Access, 2022 Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and
Ivan Vaccari +4 more
doaj +2 more sources
Adversarial machine learning phases of matter
Quantum Frontiers, 2023 We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations:
Si Jiang, Sirui Lu, Dong-Ling Deng
doaj +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
Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
Algorithms, 2021 Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology.
Shruti Patil +2 more
exaly +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
Politics of Adversarial Machine Learning [PDF]
SSRN Electronic Journal, 2020 In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights.
Kendra Albert +3 more
openaire +3 more sources
Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification [PDF]
, 2023 Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks.
Panagiotis Andriotis +8 more
core +1 more source