A state-of-the-art universal machine learning framework for decoding suspect coded messages

Abstract

This proposed framework aims to mitigate global terrorist threats, suspicious activities, and legal violations by introducing an innovative system that monitors encrypted short-text messages across instant messaging programs. In the current landscape, criminals, terrorists, and clandestine entities extensively employ encrypted messages for illicit endeavours on a global scale. Despite the escalating use of encrypted messages on social media platforms, a comprehensive solution for combating online crime is notably absent.The outlined framework addresses this gap by employing a cryptographic system that integrates semantic web ontology, a Predefined Dataset, and machine learning technology for encryption, decryption, and cryptanalysis. A key feature is the framework's ability to predict potential crimes by analyzing blogs for suspicious terminology conveyed through coded information. The identified information is then promptly communicated to law enforcement agencies, thereby alleviating the burden on numerous security entities and offering an efficient strategy against global crime.What sets this proposed framework apart is its unique utilization of encrypted text, a dimension that has remained unexplored in previous studies. The cohesive integration of its components expedites the process of anticipating crime types and significantly reduces the incidence of encrypted crimes on a global scale.