Recognizing Spam Domains by Extracting Features from Spam Emails using Data Mining

Recognizing Spam Domains by Extracting Features from Spam Emails using Data Mining

This paper attempts to develop an algorithm to recognize spam domains using data mining techniques with the focus on law enforcement forensic analysis. Spam filtering has been the major weapon against spam, but failed to reduce the number of spam emails sent to an indiscriminate set of recipients. The proposed algorithm accepts as input, spam mails of personal account and extracts features such...

Using Text Mining to Analyze Quality Aspects of Unstructured Data: A Case Study for "stock-touting" Spam Emails

The growth in the utilization of text mining tools and techniques in the last decade has been primarily driven by the increase in the sheer volume of unstructured texts and the need to extract useful and more importantly, quality information from them. The impetus to analyse unstructured data efficiently and effectively as part of the decision making processes within an organization has further...

Using Text Mining to Analyze Quality Aspects of Unstructured Data: A Case Study for â•œstock-toutingâ•š Spam Emails

The growth in the utilization of text mining tools and techniques in the last decade has been primarily driven by the increase in the sheer volume of unstructured texts and the need to extract useful and more importantly, quality information from them. The impetus to analyse unstructured data efficiently and effectively as part of the decision making processes within an organization has further...

Learning Spam Features Using Restricted Boltzmann Machines

Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by means of Restricted Boltzmann Machines (RBMs) in order to identity whether such messages contain relevant (ham) or non-relevant (spam) content. The main idea contribution of this...

Mitigating spam using network-level features