Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of
network traffic data and memory space required is usually large. It is, therefore, almost …

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in
future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly …

Abstract The Internet of Things (IoT) is increasingly utilized in daily life and industrial
production, particularly in critical infrastructures. IoT cybersecurity has an effect on people's …

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent
transceiver for future wireless communications. Blind MC has several applications in the …

Emerging wireless networks may suffer severe security threats due to the ubiquitous access
of massive wireless devices. Specific emitter identification (SEI) is considered as one of the …

The proliferation of wireless sensor networks (WSNs) and their applications has attracted
remarkable growth in unsolicited intrusions and security threats, which disrupt the normal …

Due to the implementation and performance limitations of centralized learning automatic
modulation classification (CentAMC) method, this paper proposes a decentralized learning …

Due to the computing capability and memory limitations, it is difficult to apply the traditional
deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic …

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of
distributed intelligence are promising research paradigms in the design of smart cities. IoT …

Automatic modulation classification (AMC) is one of the major challenges for cognitive radio
(CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and …