Results 71 to 80 of about 109,015 (219)

Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model

International Journal of Distributed Sensor Networks, 2016
As WSNs gain popularity, they are becoming more and more necessary for traffic anomaly detection. Because worms, attacks, intrusions, and other kinds of malicious behaviors can be recognized by traffic analysis and anomaly detection, WSN traffic anomaly ...
Qin Yu, Jibin Lyu, Lirui Jiang, Longjiang Li   +3 more
doaj   +1 more source

Machine learning for Internet of things anomaly detection under low-quality data

International Journal of Distributed Sensor Networks, 2022
With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security.
Shangbin Han, Qianhong Wu, Yang Yang
doaj   +1 more source

Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework [PDF]

, 2023
openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention.
BUGARIN, NIKOLA
core  

Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

IEEE Access
Anomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic.
Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz   +3 more
doaj   +1 more source

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]

PLoS ONE, 2016
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
doaj   +1 more source

Anomaly-Detection in Diabetes using SVM

, 2018
It's is an predominant anomaly detection technique , which is compared with many anomaly detection ...
Dr Jabez Jones (5190476)
core   +1 more source

Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction

IEEE Access
Hyperspectral anomaly detection is a detection of abnormal targets in a region based on spectral and spatial information under the premise of no prior knowledge of the target, which is a very important research topic in the field of remote sensing.
Jiao Jiao, Longlong Xiao, Chonglei Wang
doaj   +1 more source

Hyperspectral anomaly detection: a performance comparison of existing techniques

International Journal of Digital Earth, 2022
Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the use of ...
Noman Raza Shah, Abdur Rahman M. Maud, Farrukh Aziz Bhatti, Muhammad Khizer Ali, Khurram Khurshid, Moazam Maqsood, Muhammad Amin   +6 more
doaj   +1 more source

Unsupervised clustering approach for network anomaly detection

, 2012
This paper describes the advantages of using the anomaly detection approach over the misuse detection technique in detecting unknown network intrusions or attacks.
Syarif, Iwan, Prugel-Bennett, Adam, Wills, Gary B.   +2 more
core  

Network Anomaly Detection Using Federated Learning and Transfer Learning

, 2020
Since deep neural networks can learn data representation from training data automatically, deep learning methods are widely used in the network anomaly detection.
Jian Teng, Guo, Qianling, Ying Zhao, Qianling Guo, Di Wu, Chen, Junjun, Teng, Jian, Wu, Di, Junjun Chen, Zhao, Ying   +9 more
core   +1 more source