AEROSTRIKE: A REAL-TIME AI-DRIVEN FRAMEWORK FOR WIRELESS NETWORK THREAT DETECTION AND EXPLOITATION

Abstract

The rapid expansion of wireless are expending rapidly connecting millions of devices, homes, and industries globally. However, this immense growth has brought with it a significant security crisis as a traditional methods for finding network weaknesses are too slow and difficult to use. Most security experts still rely on manual tools which require typing many commands, and that can often lead to mistakes and let dangerous things through. We introduce Aerostrike, a user- friendly system that automate the entire security scanning process using Artificial Intelligence (AI). Instead of using different tools for different tasks, Aerostrike combines network scanning, threat detection, and password testing into single smart platform. It uses a machine learning method called Random Forest to accurately identify different types of devices and traffic, while other method called Isolation Forest automatically detect unusual behavior that may indicates an attack. By automatically handling these complex tasks, Aerostrike helps security teams find threats and vulnerabilities much faster and with lower error. Our tests show that this system is more reliable than manual methods and helps users quickly solve problems with clear, AI driven advice.