Evaluation of AES-256 Encryption and Machine Learning for Securing GSM Communications against Sniffing Attacks

Long notorious as one of the most popular mobile communication standards to be found around the world, the Global System for Mobile Communications (GSM) has been in widespread use for a long time now. A GSM network has been designed to have security in mind but there is still a large number of security threats to the GSM network including GSM sniffing attacks. GSM attacks rely on the weaknesses in the encryption protocols, such as A5/1, and A5/2 which can be brute forced to succeed. Sensitive data including voice calls, text messages, and location information are at great risk of interception by unauthorized parties and vulnerabilities. This research examines GSM network vulnerabilities, sniffing attacks, and the boundaries of conventional encryption protocols. This analyzes current mitigation strategies and further presents advanced countermeasures, such as mandating the use of AES 256 encryption that offers a more powerful defense against brute force attacks. It also investigates the use of machine learning for real-time detection of sniffing attacks. However, machine learning algorithms can detect unusual network activity and allow timely intervention to prevent unauthorized access. The implications of these attack methodologies and performance metrics are also included in the study, along with a complete analysis of different mitigation strategies and their performance to enhance GSM security. Increasing threats motivate the research to enhance the confidentiality, integrity, and availability of GSM communications.