Quantum-Enhanced Security Intelligence Platform with Multi-Channel Alert System and Web-based Threat Response Interface

The recent trends in cybersecurity environments imply that the traditional intrusion detection systems (IDS) can become inefficient to detect complicated and new attacks. The current project introduces a hybrid intrusion detection system based on deep learning and reinforcement learning (RL) to improve the security monitoring of the network through these methods. The system has been combined with supervised deep neural classifier and implemented RL-based decision strategies to detect and classify network intrusion precisely. The classification model with the architecture of a multi-layer neural network including dropout and batch normalization learns to discriminate between normal and malicious traffic. Moreover, two other agents, namely, an Enhanced Classification Agent and an Improved Deep Q-Network (DQN) Agent, are given to represent exploration and exploitation activities in learning. The system is satisfied with a Flask web interface and is implemented so that a user would be able to upload network data and obtain a real-time result regarding intrusion detection. The results of evaluation indicate a high level of accuracy of classification and efficiency of reward-based learning, which also makes this direction a promising one in the field of intelligent network intrusion detection systems.