The exponential growth of biomedical data in recent years has urged the application of
numerous machine learning techniques to address emerging problems in biology and …

Molecular representations play critical roles in researching drug design and properties, and
effective methods are beneficial to assisting in the calculation of molecules and solving …

The nature of heterophilous graphs is significantly different from that of homophilous graphs,
which causes difficulties in early graph neural network (GNN) models and suggests …

System auditing provides a low-level view into cyber threats by monitoring system entity
interactions. In response to advanced cyber-attacks, one prevalent solution is to apply data …

This study focuses on the potential capability of numerous machine learning models, namely
CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree …

Insider threats are malicious acts that can be carried out by an authorized employee within
an organization. Insider threats represent a major cybersecurity challenge for private and …

Advanced Persistent Threats (APT) involve multiple attack steps over a long period, and
their investigation requires analysis of myriad logs to identify their attack steps, which are a …

In recent years, the application of the internet of things (IoT) in areas such as intelligent
transportation, smart cities, and the industrial internet has become increasingly widespread …

Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

There is an astounding growth in the adoption of machine learners (MLs) to craft intrusion
detection systems (IDSs). These IDSs model the behavior of a target system during a …