A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Fraud is increasing dramatically with the expansion of modern technology and the global
superhighways of communication, resulting in the loss of billions of dollars worldwide each …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Outlier detection has been used for centuries to detect and, where appropriate, remove
anomalous observations from data. Outliers arise due to mechanical faults, changes in …

There is often the need to update an installed intrusion detection system (IDS) due to new
attack methods or upgraded computing environments. Since many current IDSs are …

In this paper we discuss our research in develo** general and systematic methods for
intrusion detection. The key ideas are to use data mining techniques to discover consistent …

One method for detecting fraud is to check for suspicious changes in user behavior. This
paper describes the automatic design of user profiling methods for the purpose of fraud …

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses
the cost of misclassifications to update the training distribution on successive boosting …

We describe the results achieved using the JAM distributed data mining system for the real
world problem of fraud detection in financial information systems. For this domain we …

Intrusion detection (ID) is an important component of infrastructure protection mechanisms.
Intrusion detection systems (IDSs) need to be accurate, adaptive, and extensible. Given …