Intelligent Machine Learning Method for Detecting Suspicious Encoded Messages

Abstract

With the use of an Advanced Machine Learning (AML) approach for suspicious coded message detection such as cryptanalysis SR4S or any messaging system, a framework to prevent worldwide crime, including terrorist attacks, bomb explosions, drone attacks, and all large and minor assaults. Terrorists communicate with their teammates in different parts of the world and give instructions with resources to attack the government or the public. To maintain secrecy, they use encrypted messages. The proposed framework at the server site will detect encrypted messages and decrypt them using Rail fence Cipher techniques. There is currently no proper solution to prevent a large crime across the globe. Suspicious Message Detection (SMD) is employed when a server receives an encoded message; the proposed system decrypts the message and accelerates the process of predicting the type of crime from microblogs before it is committed by criminals. The security sector will be less tense as a result of the dissemination of information regarding criminals.