Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
A systematic review of anomaly detection using machine and deep learning techniques
Anomaly detection identifies objects or events that do not behave as expected or correlate
with other data points. Anomaly detection has been used to identify and investigate …
with other data points. Anomaly detection has been used to identify and investigate …
A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023
Almost 85% of companies polled said they were looking into anomaly detection (AD)
technologies for their industrial image anomalies. The present problem concerns detecting …
technologies for their industrial image anomalies. The present problem concerns detecting …
Recurrent neural networks for colluded applications attack detection in android os devices
The paper presents a design and an implementation of an intelligent detector of a novel"
colluded applications" attack on user's privacy in Android OS devices, which employs …
colluded applications" attack on user's privacy in Android OS devices, which employs …
[图书][B] Integrated Framework for Data Quality and Security Evaluation on Mobile Devices
I Khokhlov - 2020 - search.proquest.com
Data quality (DQ) is an important concept that is used in the design and employment of
information, data management, decision making, and engineering systems with multiple …
information, data management, decision making, and engineering systems with multiple …