Results 61 to 70 of about 8,328,816 (311)

Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems

, 2020
Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data ...
Vähäkainu, Petri, Lehto, Martti, Kariluoto, Antti   +2 more
core   +1 more source

Fortifying Your Defenses: Techniques to Thwart Adversarial Attacks and Boost Performance of Machine Learning-Based Intrusion Detection Systems

, 2023
Machine learning has seen significant advancements in recent years and has proven to be highly effective in a wide range of applications, including intrusion detection systems (IDS).
Lou, Wenjing
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +1 more source

Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

ACM Computing Surveys
Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning (AML) attacks. Execution-time AML attacks against MARL are complex due to effects that propagate across time and between agents.
Maxwell Standen, Junae Kim, Claudia Szabo   +2 more
semanticscholar   +1 more source

On the Generalization Analysis of Adversarial Learning

, 2022
Many recent studies have highlighted the susceptibility of virtually all machine-learning models to adversarial attacks. Adversarial attacks are imperceptible changes to an input example of a given prediction model. Such changes are carefully designed to
Mustafa, Waleed, Kloft, Marius, Lei, Yunwen   +2 more
core  

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions" [PDF]

, 2009
Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc.
Gargiulo, Francesco
core   +1 more source

Breaking Machine Learning Models with Adversarial Attacks and its Variants

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Machine learning models can be by adversarial attacks, subtle, imperceptible perturbations to inputs that cause the model to produce erroneous outputs.
Pavan Reddy
doaj   +1 more source

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward [PDF]

IEEE Communications Surveys and Tutorials, 2019
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.
A. Qayyum, Muhammad Usama, Junaid Qadir, Ala I. Al-Fuqaha   +3 more
semanticscholar   +1 more source