A proposed framework presents a fresh way to addressing global concerns such as terrorism, suspicious activity, and law and order infractions by using multilingual instant messaging services to track short-text messages. There is a considerable barrier to detection and prevention due to the fact that criminals, terrorists, and those involved in unlawful operations typically use other languages to discuss their plans. While multilingual content is commonplace on social media sites, the existing methods available are insufficient to effectively combat online crime. Criminals use a variety of languages to communicate with each other and coordinate their global operations. Prior research into messaging apps has mainly concentrated on detecting suspicious communications inside a single language, ignoring the complexity of predicting suspicious messages across many languages at once. The suggested approach incorporates a multilingual strategy comprising several elements such as semantic web ontology, a database of suspicious actions with specified judgment rules, machine learning algorithms, and language translators informed by past learning experiences. This framework quickly determines the nature of the crime being discussed in microblogs when a user uses suspicious wording in a cross-lingual setting. Then, the cybercrime division receives timely updates on the criminals, reducing the workload of other security agencies. This cutting-edge system effectively identifies and prevents criminal behaviors through multilingual instant messaging services on a global scale, making it possible to oppose terrorism, suspicious crimes, and law and order breaches.

Artificial Intelligence (AI), Multilingual Machine Translation (MMT), Machine Learning (ML); Statistical Natural Language Processing (SNLP); Instant Messenger-based Social Networking; Association Rule Mining (ARM); Suspicious Communication Detection System; SCDs.