Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Stock trend prediction is one of the most widely investigated and challenging problems for
investors and researchers. Since the convolutional neural network (CNN) was introduced to …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …

Context Recent years have seen a lot of attention into Deep Learning (DL) techniques used
to detect cybersecurity attacks. DL techniques can swiftly analyze massive datasets, and …

Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML)
techniques have been widely used for botnet detection with flow-based features. The prime …

Graph neural networks (GNNs) have achieved great success in many graph-based tasks.
Much work is dedicated to empowering GNNs with adaptive locality ability, which enables …

Nowadays, a massive number of people are involved in various social media. This fact
enables organizations and institutions to more easily access their audiences across the …