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Protecting Internet of things (IoT) devices against cyber attacks is imperative owing to inherent security vulnerabilities. These vulnerabilities can include a spectrum of sophisticated attacks that pose significant damage to both…
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most…
Since the advent of the Internet of Things (IoT), exchanging vast amounts of information has increased the number of security threats in networks. As a result, intrusion detection based on deep learning (DL) has been developed to achieve…
With the rapid growth of IoT devices, ensuring robust network security has become a critical challenge. Traditional intrusion detection systems (IDSs) often face limitations in detecting sophisticated attacks within high-dimensional and…
The widespread adoption of cloud computing, edge, and IoT has increased the attack surface for cyber threats. This is due to the large-scale deployment of often unsecured, heterogeneous devices with varying hardware and software…
The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened…
Cybersecurity remains a critical challenge in the digital age, with network traffic flow anomaly detection being a key pivotal instrument in the fight against cyber threats. In this study, we address the prevalent issue of data integrity in…
With the widespread adoption of the Internet of Things (IoT) and Industrial IoT (IIoT) technologies, network architectures have become increasingly complex, and the volume of traffic has grown substantially. This evolution poses significant…
This paper proposes a novel federated learning approach for improving IoT network intrusion detection. The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to…
Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This study evaluates the transferability of three widely used…
Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data…
Through the generalization of deep learning, the research community has addressed critical challenges in the network security domain, like malware identification and anomaly detection. However, they have yet to discuss deploying them on…
The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates…
Currently, it is a hot research topic to realize accurate, efficient, and real-time identification of massive spectral data with the help of deep learning and IoT technology. Deep neural networks played a key role in spectral analysis.…
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource…
The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy…
Spectrum scarcity has surfaced as a prominent concern in wireless radio communications with the emergence of new technologies over the past few years. As a result, there is growing need for better understanding of the spectrum occupancy…
The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare has introduced unique cybersecurity challenges, primarily due to the diverse communication protocols and critical nature of these devices This research aims…
Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high…
Intrusion Detection Systems (IDSs) have played a significant role in the detection and prevention of cyber-attacks in traditional computing systems. It is not surprising that this technology is now being applied to secure Internet of Things…