Authors - Niharika Terli, Pavan Chintakayala, Venu Madhavi Angaluri, Suhasini Sodagudi Abstract - The Growth of electronic gadget usage has been rapidly increasing day by day. Around 80% of people are dependent on mobile devices for accessing, storing, and sharing information. This requires security which is critical to managing. In this regard, security is missing due to the presence of spam messages in mobility devices. It is important to detect spam messages because it contains unnecessary information and data leakage could be possible. It is used to develop two different solutions to this problem by using machine learning algorithms, for finding the Greek word we use Snowball Stemmer Algorithm, Porter Stemmer, and use some machine learning techniques like the TF_IDF algorithm to increase accuracy. To combine these two, we use a multinational Naive Bayes algorithm, a Support Vector Machine. A vectorized model is obtained then we can classify the messages. Upon completion of the project, the society can be secure from hackers, protect society against viruses in their devices, and also unwanted malicious behavior can be identified which will be affecting the social network users.