Results 31 to 40 of about 8,328,816 (311)
EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
doaj +1 more source
Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN) [PDF]
Journal of Network and Computer Applications, 2022 O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse ...
Edan Habler +8 more
semanticscholar +1 more source
Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives
, 2022 Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications.
Jadidi, Z, Pal, S, Selvakkumar, A
core +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 in Smart Energy Systems [PDF]
, 2019 Smart Energy Systems represent a radical shift in the approach to energy generation and demand, driven by decentralisation of the energy system to large numbers of low-capacity devices.
Bor, Martin +11 more
core +1 more source
Adversarial Machine Learning for 5G Communications Security [PDF]
Game Theory and Machine Learning for Cyber Security, 2021 Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization.
Y. Sagduyu, T. Erpek, Yi Shi
semanticscholar +1 more source
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective [PDF]
ACM Computing Surveys, 2020 Deep Learning algorithms have achieved state-of-the-art performance for Image Classification. For this reason, they have been used even in security-critical applications, such as biometric recognition systems and self-driving cars.
G. R. Machado +2 more
semanticscholar +1 more source
Law and Adversarial Machine Learning
CoRR, 2018 When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks ...
Ram Shankar Siva Kumar +3 more
openaire +3 more sources
Adversarial Machine Learning in Network Intrusion Detection Systems [PDF]
Expert systems with applications, 2020 Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output.
Elie Alhajjar +2 more
semanticscholar +1 more source
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Anomaly detection in videos is an important computer vision problem with various applications including auto-mated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on ...
Furkan Mumcu, Keval Doshi, Yasin Yılmaz
semanticscholar +1 more source