Create an Intrusion Detection System to Detect Threats in Public WiFi Networks

Public wireless networks are currently one of the vectors for security threats such as unauthorized access and injection of malware. The current systems have mostly signature-based approaches that prove inefficient against novel and evolving threats. Consequently, significant vulnerabilities arise because users remain at risk of data breaches and privacy violations. We thus design a hybrid IDS by merging signature-based detection with advanced LSTM for sequential anomaly detection and Random Forest or XGBoost for strong classification of traffic patterns. The system is designed to detect in real time as known as unknown threats with the help of deep packet inspection and behavioral analysis of network traffic. LSTMs capture temporal dependencies in network behavior, while Random Forest/XGBoost efficiently classifies anomalous patterns. This solution is expected to raise the accuracy of detection, minimize false positives, and build dynamic adaptability for emerging threats, thereby enhancing public WiFi network security and protection of user data by a much higher margin.