Spam Detection using Recurrent Neural Networks

Spam is well defined as the unsolicited bulk messages or junk mail will send to email address or phone number that are generally marketable in nature and also carry malicious documents. The main issue of spam is that it can download malicious files which can attack the computers, smart phones and networks, utilize network bandwidth and storage space, degrades email servers and can cause attacks in our devices like spyware, phishing and ransomware. In the existing approach, an exploratory analysis of supervised machine learning algorithms has done and the performance has been evaluated. The drawback of existing approach is that the performance of supervised machine learning algorithms decreases as we increase the size of the dataset. In order to overcome such drawbacks, an efficient spam detection using recurrent neural networks using the BiGRU model has been proposed. By implementing this, it has been achieved with better accuracy of 99.07%. From this, it is concluded that BiGRU model has better performance than existing approaches.