At the confluence of two great paradigms such as Edge Computing and Artificial Intelligence,
Edge Intelligence arises. This new concept is about the smart exploitation of Edge …

As the Internet of Things (IoT) rapidly proliferate in the world, new attacks exploiting the
weaknesses of the unfledged IoT technologies are emerging constantly. An Intrusion …

The growing adoption of the Internet of Things (IoT) has brought a significant increase in
attacks targeting those devices. Machine learning (ML) methods have shown promising …

Network-based intrusion detection system (NIDS) monitors network traffic for malicious
activities, forming the frontline defense against increasing attacks over information …

The rapid integration of IoT, cloud, and edge computing has resulted in highly
interconnected networks, emphasizing the need for advanced Intrusion Detection Systems …

The effectiveness of network intrusion detection systems, predominantly based on machine
learning, is highly influenced by the dataset they are trained on. Ensuring an accurate …

Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) attacks pose
significant threats to the security of Software-Defined Internet of Things (SD-IoT) networks …

Recently, the Internet of Things (IoT) has been used in various applications such as
manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and …

While promoting the development of the Internet of Things, cloud‐fog hybrid computing
faces severe information security risks. The intrusion detection system deployed in the fog …

Traffic classification, which deduces task-relevant traffic types, is required for network
management, security, and quality of service (QoS). In recent studies, deep learning has …