Results 61 to 70 of about 5,452,269 (307)
Establishment and mapping of heterogeneous anomalies in network intrusion datasets
Connection Science, 2022 Anomaly detection in the scope of network security aims to identify network instances for the unexpected and unique, with various security operations employing such techniques to facilitate effective threat detection.
Liam Riddell +2 more
doaj +1 more source
FEBS Letters, EarlyView.This study reveals a unique active site enriched in methionine residues and demonstrates that these residues play a critical role by stabilizing carbocation intermediates through novel sulfur–cation interactions. Structure‐guided mutagenesis further revealed variants with significantly altered product profiles, enhancing pseudopterosin formation. These
Marion Ringel +13 more
wiley +1 more source
Spoofing Attack Detection by Anomaly Detection [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks.
Fatemifar, Soroush +3 more
openaire +3 more sources
FEBS Open Bio, EarlyView.Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
, 2018 One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications.
A Zimek +15 more
core +1 more source
A Diffusion-Based Framework for Multi-Class Anomaly Detection
AAAI Conference on Artificial IntelligenceReconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of ...
Haoyang He +8 more
semanticscholar +1 more source
International Journal of Computer Vision, 2021 The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new ...
Paul Bergmann +4 more
semanticscholar +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang +10 more
wiley +1 more source
Cross correlation anomaly detection system [PDF]
, 1975 This invention provides a method for automatically inspecting the surface of an object, such as an integrated circuit chip, whereby the data obtained by the light reflected from the surface, caused by a scanning light beam, is automatically compared with
Micka, E. Z.
core +1 more source
Quantum machine learning for quantum anomaly detection
, 2017 Anomaly detection is used for identifying data that deviate from `normal' data patterns. Its usage on classical data finds diverse applications in many important areas like fraud detection, medical diagnoses, data cleaning and surveillance.
Liu, Nana, Rebentrost, Patrick
core +1 more source