Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

The future Internet of Things (IoT) will have a deep economical, commercial and social
impact on our lives. The participating nodes in IoT networks are usually resource …

The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …

The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Conventional attacks of insider employees and emerging APT are both major threats for the
organizational information system. Existing detections mainly concentrate on users' behavior …

Insider threats are one of today's most challenging cybersecurity issues that are not well
addressed by commonly employed security solutions. In this work, we propose structural …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Stories of cyber attacks are becoming a routine in which cyber attackers show new levels of
intention by sophisticated attacks on networks. Unfortunately, cybercriminals have figured …

Data imbalance remains one of the most widespread problems affecting contemporary
machine learning. The negative effect data imbalance can have on the traditional learning …