Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Sarah M. Erfani"'
Publikováno v:
IEEE Access, Vol 12, Pp 121971-121982 (2024)
Anomaly detection in graphs is increasingly used to reveal fraud, fakes, security attacks and unusual behaviours in networks, such as social networks, financial transaction networks and the Internet of Things. Accurately detecting such graph anomalie
Externí odkaz:
https://doaj.org/article/c2d5bbab100e4f9f84685f1fc17dba18
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 4, Pp 1-19 (2023)
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems, such as classification and identification tasks. A subclass of QML methods is quantum generative adver
Externí odkaz:
https://doaj.org/article/4d28a464cc024ac6b9876dbf6567865a
Autor:
Maxwell T. West, Sarah M. Erfani, Christopher Leckie, Martin Sevior, Lloyd C. L. Hollenberg, Muhammad Usman
Publikováno v:
Physical Review Research, Vol 5, Iss 2, p 023186 (2023)
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology, and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malici
Externí odkaz:
https://doaj.org/article/5a4293add51c45b8a72693e77d943aa8
Autor:
Maxwell T. West, Shu-Lok Tsang, Jia S. Low, Charles D. Hill, Christopher Leckie, Lloyd C. L. Hollenberg, Sarah M. Erfani, Muhammad Usman
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce521b0f56ad1548d5c293aade15ccd9
Autor:
Sandamal Weerasinghe, Tamas Abraham, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein
Publikováno v:
2022 IEEE 61st Conference on Decision and Control (CDC).
Publikováno v:
IEEE Transactions on Information Forensics and Security. 16:2566-2578
Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations such as poisoning attacks and label flips. By carefully manipulating a subset of training samples, the attacker forces the learner to compute an incorrect decision
Publikováno v:
Network and System Security ISBN: 9783031230196
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::815011ee9487b2f56936bfedc6d4fe0c
https://doi.org/10.1007/978-3-031-23020-2_6
https://doi.org/10.1007/978-3-031-23020-2_6
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:815-820
We present a new unsupervised dimensionality reduction technique, called LN-SNE, for anomaly detection. LN-SNE generates a parametric embedding by means of Restricted Boltzmann Machines and uses a heavy-tail distribution to project data to a lower di
Publikováno v:
CIKM
Incremental contrast pattern mining (CPM) is an important task in various fields such as network traffic analysis, medical diagnosis, and customer behavior analysis. Due to increases in the speed and dimension of data streams, a major challenge for C
Autor:
Benjamin I. P. Rubinstein, Sarah M. Erfani, Tamas Abraham, Christopher Leckie, Tansu Alpcan, Sandamal Weerasinghe
Publikováno v:
IJCAI
Nonlinear regression, although widely used in engineering, financial and security applications for automated decision making, is known to be vulnerable to training data poisoning. Targeted poisoning attacks may cause learning algorithms to fit decisi