Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

Federated learning allows multiple clients to jointly train a model on their private data
without revealing their local data to a centralized server. Thereby, federated learning has …

Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …

Data stream mining presents notable challenges in the form of concept drift and evolution.
Existing learning algorithms, typically designed within a supervised learning framework …

Learning from non-stationary data streams is inherently challenging due to their evolving
nature and concept drift. Furthermore, the assumption that all instances come labeled is …

Data stream classification is an important problem in the field of machine learning. Due to
the non-stationary nature of the data where the underlying distribution changes over time …

Abstract Machine learning based intrusion detection systems monitor network data streams
for cyber attacks. Challenges in this space include detecting unknown attacks, adapting to …

Technology has amplified malware activity, affecting network and users. Before being
forwarded to the next host, network traffic must be dynamically analysed for malware. By …

In cybersecurity, live production data for predictive analysis pose a significant challenge due
to the inherently secure nature of the domain. Although there are publicly available …

In real-world scenarios classification models are often required to perform robustly when
predicting samples belonging to classes that have not appeared during its training stage …