Results 11 to 20 of about 33,606 (255)

Quantum adversarial machine learning

Physical Review Research, 2020
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
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   +2 more sources

Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

Journal of Cybersecurity and Privacy, 2021
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
Pavlos Papadopoulos, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Alexios Mylonas, William J. Buchanan   +5 more
doaj   +3 more sources

Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review

Applied Computer Systems, 2021
In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning.
Mahima K. T. Y., Ayoob Mohamed, Poravi Guhanathan   +2 more
doaj   +1 more source

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, Jingxuan Wang, Yuhe Wang, Guohui Zhou, Yan Zhao   +4 more
doaj   +1 more source

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification

, 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, McCarthy, Andrew, Phil Legg, Andrew McCarthy, Ghadafi, Essam, Andriotis, Panagiotis, Andriotis, Panagiotis; id_orcid, Legg, Phil, Essam Ghadafi   +8 more
core   +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 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, Molineux, Andy, Angelos K. Marnerides, Roedig, Utz, Bor, Martin C., Wattam, Steve, Martin C. Bor, Utz Roedig, Steve Wattam, Marnerides, Angelos K., Marnerides, Angelos, Andy Molineux   +11 more
core   +1 more source

Adversarial Machine Learning

, 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 ...
Serban, A.C., Buhan, I., Hernández-Castro, C.J., Picek, S., Tsingenopoulos, I., Batina, L., Bäck, T., Liu, Z., Joosen, W.   +8 more
core   +1 more source

Adversarial Attacks and Defenses in Deep Learning

Engineering, 2020
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu   +3 more
doaj   +1 more source