Detection of Cybercriminal Activities in Smartphones via NLP-Based Communication Pattern Analysis

Smartphones have become a very important part as they handle sensitive personal and financial information in our everyday life. Given their prominence, they are susceptible to various cyber threats, including identity theft, fraud, and phishing. At times, traditional cybersecurity solutions may struggle to tackle the complex threats that arise from communication. The paper presents a new method to enhance the identification of smartphone threats by combining Long Short-Term Memory networks with NLP. This study demonstrates the ability of LSTMs to detect nuanced signs of cybercrime and analyze complex communication patterns using a large dataset of anonymous communications from various sources. The proposed model demonstrates an exceptional accuracy rate of 98.93%, surpassing traditional methods and other machine learning models. This approach highlights the advantages of utilizing BBAD and natural language processing (NLP) to detect and understand malicious activities.