SCMD suspicious cryptographic message detection

Abstract

The proposed framework aims to prevent global crimes, specifically terrorist attacks, bomb blasts, and drone attacks, by using the Suspicious Cryptographic Message Detection (SCMD) system to detect and decrypt suspicious messages. Terrorists often use encrypted messages to communicate with their teammates in different parts of the world, making it difficult for authorities to monitor their activities. The framework addresses this issue by detecting with gradient boosting algorithm and cryptanalyzing by using Simple Substitution Cipher techniques to decrypt suspicious messages and predict potential criminal activity. The framework utilizes various emerging trends in information technology, including machine learning techniques, pre-defined decision rules, wordnet, and semantic web ontology, to predict the type of crime, the criminal's name and location, and other criminal details. It comprises multiple components, including Semantic web Ontology, Suspicious Database, and machine learning techniques, to facilitate the detection and reporting of criminal activity. When the server receives a suspicious encrypted message, the framework detect and decrypts it and predicts the type of crime based on microblogs before it can be executed by the criminals. The details of the criminals are then alerted to the cybercrime department, reducing the burden on security departments and enhancing public safety. In summary, the framework provides a comprehensive solution to the challenges posed by encrypted messages in criminal activities. It uses advanced technologies to identify and predict potential criminal activities, enabling law enforcement agencies to take preemptive actions to prevent major and minor attacks, including terrorist attacks.