With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based...

Content-Centric Networks (CCNs) have recently emerged as an innovative trend to overcome many inherent security problems in the IP-based (host-based) networks by securing the content itself rather than the channel through which it travels. In this network architecture new kinds of attacks -ranging from DoS to privacy attackswill appear. Therefore, it is becoming necessary to design a flexible a...

In clustering algorithm research, objects, attributes and other aspects of data sets are usually considered to be independent identically distributed; that is, each object is assumed an uniformly distributed individual with no impacts between objects. However, objects in real life often neither independently nor they non-IID, leading a complex coupling relationship interact other. The results u...

The purity of the dataset used for model construction plays important roles in the accuracy and reliability of model building; outliers are often caused by noisy data as a result of mechanical faults, changes in system behaviour, or due to human error. This is why it is essential to pre-process dataset prior to modelling, in order to differentiate between data that appears normal or abnormal wi...

We propose a unique and hybrid approach containing data mining techniques, artificial intelligence and statistics in a single platform for fraud detection of online financial transaction, which combines evidences from current as well as past behavior. The proposed transaction risk generation model (TRSGM) consists of five major components, namely, DBSCAN algorithm, Linear equation, Rules, Data ...