Optimized near Duplicate Matching scheme for E-mail Spam Detection
نویسندگان
چکیده
Today the major problem that the people are facing is spam mails or e-mail spam. In recent years there are so many schemes are developed to detect the spam emails. Here the primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by users feedback, to block the subsequent near-duplicate spam’s. We propose a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, we design a complete spam detection system Cosdes (standing for Collaborative Spam Detection System), which possesses an efficient near-duplicate matching scheme and a progressive update scheme. To detect fastly near duplicates and duplicate spam mails in Cosdes , we propose a new approach SimHash.
منابع مشابه
Improved near Duplicate Matching Scheme for E-mail Spam Detection
Today the major problem that the people are facing is spam mails or e-mail spam. In recent years there are so many schemes are developed to detect the spam emails. Here the primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user’s feedback, to block the subsequent near-duplicate spam’s. We propose a novel e-mail abstraction scheme, ...
متن کاملAccurate Spam Mail Detection
With the increasing popularity of a E-mail users, E-mail spam problem growing proportionally. Spam filtering with near duplicate matching scheme is widely discussed in recent years. It is based on a known spam database formed by user feedback which cannot fully catch the evolving nature of spam and also it requires much storage. In view of above drawbacks, we proposed an effective spam detectio...
متن کاملA New Hybrid Approach of K-Nearest Neighbors Algorithm with Particle Swarm Optimization for E-Mail Spam Detection
Emails are one of the fastest economic communications. Increasing email users has caused the increase of spam in recent years. As we know, spam not only damages user’s profits, time-consuming and bandwidth, but also has become as a risk to efficiency, reliability, and security of a network. Spam developers are always trying to find ways to escape the existing filters therefore new filters to de...
متن کاملA Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization
Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...
متن کاملEmail Spam Detection Using Customized SimHash Function
E-mail communication is a narrative challenging in present days, because a problem can be done in that communication from one to other emails process generation. The problem is spam mail combination in original mail interaction. This is the major task for sending information from one to other persons, if it important to that particular person. So to solve these problems effectively traditionall...
متن کامل