Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense,
alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to …

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to pose
significant threats to networked systems, causing disruptions that can lead to substantial …

As the digital landscape expands rapidly due to technological advancements, cybersecurity
concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are …

Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to
software-defined networks (SDNs). This paper proposes a feature-engineering-and machine …

This work exploits the concept of one-class classifier applied to the problem of anomaly
detection in communication networks. The article presents the design of an innovative …

The incorporation of advanced networking technologies makes modern systems vulnerable
to cyber-attacks that can result in a number of harmful outcomes. Due to the increase of …

This paper presents the FlowTransformer framework, a novel approach for implementing
transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer …

Ensuring the privacy and trustworthiness of smart city—Internet of Things (IoT) networks
have recently remained the central problem. Cyborg intelligence is one of the most popular …

Large unbalanced datasets pose challenges for machine learning models, as redundant
and irrelevant features can hinder their effectiveness. Furthermore, the performance of …

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its
emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT …