Many anomaly detection techniques have been adopted by Industrial Internet of Things
(IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are …

The dynamics of computer networks have changed rapidly over the past few years due to a
tremendous increase in the volume of the connected devices and the corresponding …

Intrusion detection systems are one of the security tools widely deployed in network
architectures in order to monitor, detect and eventually respond to any suspicious activity in …

Attributed networks are a type of graph structured data used in many real-world scenarios.
Detecting anomalies on attributed networks has a wide spectrum of applications such as …

Recent IoT proliferation has undeniably affected the way organizational activities and
business procedures take place within several IoT domains such as smart manufacturing …

Anomaly detection in a network is one of the prime concerns for network security. In this
work, a novel Channel Boosted and Residual learning based deep Convolutional Neural …

Deep learning has been widely studied in many technical domains such as image analysis
and speech recognition, with its benefits that effectively deal with complex and high …

Recently developed machine learning techniques, with emphasis on deep learning, are
finding their successful implementations in detection and classification of anomalies at both …

Due to their rapid growth and deployment, Internet of things (IoT) devices have become a
central aspect of our daily lives. However, they tend to have many vulnerabilities which can …

In this paper, a hybrid approach of combing two machine learning algorithms is proposed to
detect the different possible attacks by performing effective feature selection and …