Incremental Immune-Inspired Clustering Approach to Behavior-Based Anti-Spam Technology

Facing new type of challenge which maintain clusters in a dynamic web environment with a high volume of updates and costly re-clustering, the paper describes a novel behavior-based anti-Spam technology based on incremental immune-inspired clustering algorithm. we use an “internal image” network to represent the input data set in order to reduce data redundancy, whilst at the same time extracting relevant information from the data set, It is difference with traditional clustering methods and it’s ability is to deliver the most relevant Spam from the collection of all Spam that is reported by members without having to perform complete re-clustering, Experimental evaluation shows that the novel approach provide significantly faster data summarization than completely re-clustering and the technology is reliable, efficient and scalable. Because no single technology can achieve one hundred percent Spam detection with zero false positives, it should be used in conjunction with other filtering systems to minimize errors.