Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

Recently, the frequency and complexity of ransomware attacks have been increasing
steadily, posing significant threats to individuals and organizations alike. While traditional …

The significant advancements in sensors and other resource-constrained devices, capable
of collecting data and communicating wirelessly, are poised to revolutionize numerous …

Multivariate time series anomaly detection methods can discover malfunctions in a complex
system by detecting anomalies in the monitoring data. Multivariate time series have complex …

The deep unsupervised time-series anomaly detector depends on the one-class
representation, which is more effective by only formulating the normal samples. However …

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and
approaches led to using Machine Learning (ML) techniques to build more efficient and …

Deep learning enables effective predictions. But deep structures face some challenges on
human interpretability compared to conventional techniques, eg, fuzzy inference systems. It …

With the rise of DDoS attacks, several machine learning-based attack detection models have
been used to mitigate malicious behavioral attacks. Understanding how machine learning …

Detecting botnets is an essential task to ensure the security of Internet of Things (IoT)
systems. Machine learning (ML)-based approaches have been widely used for this purpose …

The intersection of Cybersecurity and AI has garnered increasing attention in recent years
due to the growing importance of securing digital assets in an interconnected world. This …