Detection of Spambot using Random Forest Algorithm

Abstract

Spam bot detection is an automated computer program has beendesigned to mini human behaviors in the sending and spreading of spam. Spambot usually create a fraud account that can be used to send spam. In the existing work, the spambot is identified by using a social fingerprinting technique and also the Spambot account is detected by the Longest Common Subsequences (LCS) algorithm. The similarities between both genuine account and Spambot account groups of users are characterized by the digital DNA. In the LCS algorithm, the spambot detection is not verified in the efficient manner. To overcome this problem in the proposed work is going to identify the Spambot detection using Random forest classifier technique instead of LCS method from the twitter account. In addition, it will also enhance the accuracy of the classification by using Latent Semantic Analysis(LSA) and also it will detect and blocks the spambot accounts.