We propose an attack detection framework for Internet of Things (IoT) networks, which
leverages Graph Neural Networks (GNN) to capture the inherent structure of IoT network …

Internet ve kablosuz haberleşme teknolojilerinin gelişmesi paralelinde IoT alanında yapılan
çalışmalar da ilerlemektedir. Sağlık alanında kullanılan IoT sensörleri ile hastaları yakından …

Cyber threat hunting is time-consuming as large data quantities are analysed to hunt down
attacks that have evaded existing security measures. With an increasing number of Internet …

Abstract The Internet of Things (IoT) has revolutionized our lives by connecting devices to
the internet, enabling automation and simplifying daily routines. However, as IoT is built …

Since the interest to IoT systems is constantly increasing, it is vital to recognize that the IoT
data streams contain anomalies. Anomalies can be caused by system failure, network …

In the last decade and a half, machine learning has been refounded on a class of
techniques called deep learning. The earliest, most prominent techniques of deep learning …

The digital world we live in emphasises the importance of data. From an end-user
perspective, data content and availability are important as they help to meet users' demands …

Log files contain valuable information for detecting abnormal behavior. To detect anomalies,
researchers have proposed representing log files as knowledge graphs (KGs) and using KG …

Many real-world networks including the World Wide Web and the Internet of Things are
graphs in their abstract forms. Graph neural networks (GNNs) have emerged as the main …

Datacenters alongside many domains are well represented by directed graphs, and there
are many datacenter problems where deeply learned graph models may prove …