With the advent of the big data era, the detection of anomalies in time series data, especially
multidimensional time series data, has received a great deal of attention from researchers in …

This research presents a new generic deep learning (DL) framework for anomaly detection
in the Internet of Everything (IoE). It combines decomposition methods, deep neural …

This work compares the performance of different combinations of data sources for intrusion
detection in depth. To learn and distinguish between normal and malicious behavior, we use …

In-vehicle network security plays a vital role in ensuring the secure information transfer
between vehicle and Internet. The existing research is still facing great difficulties in …

Machine Learning (ML) techniques have been widely applied for network intrusion
detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer …

Network-based security has emerged as an increasingly critical challenge in the domain of
the Internet of Things (IoT). A number of network intrusion detection systems (NIDS), typically …

The role of semi-supervised network intrusion detection systems is becoming increasingly
important in the ever-changing digital landscape. Despite the boom in commercial and …

Abstract Network Intrusion Detection Systems (NIDSs) are crucial for resisting cyber threats.
However, NIDSs equipped with supervised learning models do not generalize well to …

Anomaly detection is defined as discovering patterns that do not conform to the expected
behavior. Previously, anomaly detection was mostly conducted using traditional shallow …

This research paper highlights the relationship between data-driven organizations and data
spaces and focuses on unlocking capabilities that can be used to transform organizations …