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The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to…

The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related…

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general purpose Intrusion Detection Systems (IDS) to be…

Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments. However, in Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and…

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…

Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective…

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…

In the recent years, we have witnessed a huge growth in the number of Internet of Things (IoT) and edge devices being used in our everyday activities. This demands the security of these devices from cyber attacks to be improved to protect…

In the Internet of Things (IoT) environment, continuous interaction among a large number of devices generates complex and dynamic network traffic, which poses significant challenges to rule-based detection approaches. Machine learning…

The rapidly expanding nature of the Internet of Things (IoT) networks is beginning to attract interest across a range of applications, including smart homes, smart transportation, smart health, and industrial contexts. This cutting-edge…

The rapid proliferation of Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. This has prompted ongoing research in Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for…

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT…

The increase in network attacks has necessitated the development of robust and efficient intrusion detection systems (IDS) capable of identifying malicious activities in real-time. In the last five years, deep learning algorithms have…

The rapid proliferation of Internet of Things (IoT) devices introduces significant security challenges due to limited visibility and weak device-level guarantees. Accurate and timely identification of devices is essential for enforcing…

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 need for secure Internet of Things (IoT) devices is growing as IoT devices are becoming more integrated into vital networks. Many systems rely on these devices to remain available and provide reliable service. Denial of service attacks…

Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT) environment, due to the large-scale nature of the system and its susceptibility to man-in-the-middle and eavesdropping attacks. In…

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT make it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary…

The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of "smart" objects.…

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this…