Traditional ML based IDS cannot handle high-speed and ever-evolving attacks.
Furthermore, these traditional IDS face several common challenges, such as processing …

The proliferation of Internet of Things (IoT) technology has resulted in an increase in security
vulnerabilities associated with the interconnectivity of IoT devices. As a result, there is a …

The data generated exponentially by a massive number of devices in the Internet of Things
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …

Preventing attackers from interrupting or totally stopping critical services in cloud systems is
a vital and challenging task. Today, machine learning-based algorithms and models are …

For the past few years, the concept of the smart house has gained popularity. The major
challenges concerning a smart home include data security, privacy issues, authentication …

Anomaly detection in network data is a critical task in various domains, and graph-based
approaches, particularly Graph Convolutional Networks (GCNs), have gained significant …

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by
enabling real-time monitoring, remote diagnostics, and data-driven treatment. However …

Network anomaly detection solutions can analyze a network's data volume by protocol over
time and can detect many kinds of cyberattacks such as exfiltration. We use exponential …

Computer networks are getting more complex these days. A computer network failure can
result in the loss of important data, disruption of network services and applications, and …

The concept of smart houses has grown in prominence in recent years. Major challenges
linked to smart homes are identification theft, data safety, automated decision-making for IoT …