Abstract Graph Neural Networks (GNNs) gained the attention of researchers following
advancements in Representational Learning. Unlike classical machine learning (ML) …

Network attacks are dramatically increasing over the years. A graph can accurately model
the network activities. Therefore, graph-based techniques are frequently used to detect …

Random anomalous behavior is a false or redundant behavior that randomly appears in the
network structure, affecting the analysis result of the network. Current methods mainly …

Graph neural networks (GNNs), which extend deep learning models to graph-structured
data, have achieved great success in many applications such as detecting malicious …

Vehicle-to-everything (V2X) communication is contributing towards the realization of
futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher …

With the popularity of various online dating applications, it has become a crucial task to
detect anomalous or malicious users from a large number of reciprocal users. Essentially …

Malware behavioral graphs provide a rich source of information that can be leveraged for
detection and classification tasks. In this paper, we propose a novel behavioral malware …

With the advance in the internet of things (IoT), wireless communication, and artificial
intelligence, nowadays, people can enjoy the autonomous driving system. Meanwhile …

Detecting malware using dynamic analysis techniques is an efficient method. Those familiar
techniques such as signature-based detection perform poorly when attempting to identify …

With society's increasing reliance on computer systems and network technology, the threat
of malicious software grows more and more serious. In the field of information security …