Authorship Detection in Cyber World

The main goal of this paper is to summarize and propose the idea of detecting fraud in cyber world. Here specifically we are dealing with fraud in text messages. The concept of data mining has been implemented where we have created dataset of user’s texting pattern and that data has been trained. The concept of supervise learning also comes into act, if the texting pattern of the sender doesn’t match its training data pattern then the receiver will be indicated regarding the doubtfulness of the user. This paper comes under the societal category. The application which we will be creating is going to be a cutting edge in the field of cyber world. Our application can be an answer for new authorship attribution algorithm which can exploit context, can process multi-modal data. For decades computer scientists, scholars have been jointly developing automated method to identify author based on the style of the writing. All authors possess characteristics of habit that influence the form and content of their written work. These characteristics can often be measured and quantified using machine learning methods. A comprehensive review of the method of authorship detection can be applied to the problem of social media forensics. We have provided step by step explanation for several scalable approaches. In some cases, the text of a single posted message will be only clue to an author’s identity.