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In this paper, we propose a novel approach for classifying incoming continuous data under a non-stationary environment. A class of estimators termed stochastic learning weak estimators has been generalized to include continuous time sampling and countable state categories. The method is founded on non-stationary Markov chain techniques and is useful in diverse applications, such as consumer behavior...
A sizable amount of current literature on online drift detection tools thrive on unrealistic parametric strictures such as normality or on non-parametric methods whose power performance is questionable. Using minimal realistic assumptions such as unimodality, we have strived to proffer an alternative, through a novel application of Bernstein’s inequality. Simulations from such parametric densities...
The amazing nature of biological immune systems on protecting humans from pathogens inspired people to develop artificial immune systems. Designed to simulate the functionalities of biological immune systems, artificial immune systems are suggested to be mainly applied in the domain of computer security. In this paper, we propose an artificial immune system for phishing detection. The system is to...
Trust is a phenomenon that is exclusively possessed by human beings. Due to its human-related properties, trust is difficult to be uniformly defined or even be precisely described. As a research field, trust has been intensively focused on exploring propagations and usefulness in social networks. Little research work has been found on simulating trust itself. In this paper, we present a trust computing...
Anomaly detection involves identifying observations that deviate from the normal behavior of a system. One of the ways to achieve this is by identifying the phenomena that characterize “normal” observations. Subsequently, based on the characteristics of data learned from the “normal” observations, new observations are classified as being either “normal” or not. Most state-of-the-art approaches, especially...
Social computing is the backbone of the increasing socialized web applications, which more and more people are tending to intensively rely on. Trust is a critical element for social computing that has been involved into many computational systems. It is a topic intriguing numerous studies. In this paper, we present a novel Computational Trust Framework based on the perspective of information sharing...
Phishing is continually shown to be a problem in network security. Phishing is trickery towards the user believing that he or she is communicating with a trusted source. Most phishing attacks are focused on the financial infrastructure such as financial institutions like banks and services like Paypal. We proposed a different interface than the traditional browsers which prove through past research...
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