Detection of spam and threads identification in E-mail spam corpus using content based text analytics method

In the internet world Era, Email user valuable data's are continuously increasing in spite of the evolution of mobile applications, social networks and so on. In that, Email is an essential mechanism to communicate people around the world for sharing useful financial and personal information within family, friends or even for official reasons. Hacking is intensifying nowadays due to the growth of unnecessary spam messages. The existing work handled the prevention of hacking process incompetently and to prevent unnecessary spam mail receive from spammers. In this research work, focus the robust spam filter techniques to reduce the incoming spam emails. Internet users facing numerous type of spam threads. To avoid unnecessary spam email problems in existing algorithm, the proposed algorithm classifies different type of spam threads that check the spam corpus data base using spam keywords by content text analytics method and classify the spam according to its categories. In the proposed method, extract the frequent spam threads and provides better solutions in terms of handling the ethical hacking from spammers. (C) 2019 Elsevier Ltd. All rights reserved.