This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which
enables a fully online Machine Learning (ML) based Intrusion Detection System (IDS) that …

With recent advances in the Internet of Things (IoT) technology, more people can have
instant and easy access to the IoT network of vast and diverse interconnected devices (eg …

Federated learning (FL) has become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …

In recent years, neural networks-based autoencoders have gained popularity in problems of
anomaly detection. Recent approaches have proposed ensembles of autoencoders to …

Despite the promising results of machine learning for network-based intrusion detection,
current techniques are not widely deployed in real-world environments. In general …

Autoencoder and conventional machine learning classifiers are widely used to design an
intrusion detection system (IDS). However, noise and corruption in the high-dimensional …

Internet of Things (IoT) security is a guarantee for the rapid development of IoT. Traditional
supervised deep learning-based intrusion detection systems (IDSs) need to label all traffic …

Recently, machine learning techniques, especially supervised learning techniques, have
been adopted in the Intrusion Detection System (IDS). Due to the limit of supervised …

With the development of the Industrial Internet of Things (IIoT), more frequent attacks occur
to intrude IIoT devices. A reasonably designed intrusion detection method can effectively …

The network security analyzers use intrusion detection systems (IDSes) to distinguish
malicious traffic from benign ones. The deep learning-based (DL-based) IDSes are …