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Dynamical observer for continuous linear Roesser systems
(
Elsevier B.V.
, 2020 , Conference Paper)
Monitoring of industrial systems for anomalies such as faults and cyber-attacks as unknown and extremely undesirable inputs in the presence of other inputs (like disturbances) is an important issue for ensuring the safety ...
Iterative per Group Feature Selection for Intrusion Detection
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Network security is an critical subject in any distributed network. Recently, machine learning has proven their efficiency for intrusion detection. By using a comprehensive dataset with multiple attack types, a well-trained ...
Outlier detection approaches based on machine learning in the internet-of-things
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Outlier detection in the Internet of Things (IoT) is an essential challenge issue studied in numerous fields, including fraud monitoring, intrusion detection, secure localization, trust management, and so on. Conventional ...
Machine Learning Techniques for Network Anomaly Detection: A Survey
(
IEEE
, 2020 , Conference Paper)
Nowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit ...
Hybrid Machine Learning for Network Anomaly Intrusion Detection
(
IEEE
, 2020 , Conference Paper)
In this paper, a hybrid approach of combing two machine learning algorithms is proposed to detect the different possible attacks by performing effective feature selection and classification. This system uses Random Forest ...