Deep Q-Networks Based Efficient Two - way security system for Intrusion Detection in cloud computing Environment

Cloud computing (CC) offers a range of services over the Internet on a pay-per-use basis. Consequently, many organizations have adopted this approach to attract customers with its appealing features. However, the architecture of cloud computing makes it susceptible to malicious attacks. This necessitates the deployment of an Intrusion Detection System (IDS) capable of reliably identifying such threats within the cloud environment. This study presents an effective two-way security framework that leverages Deep Q-Networks (DQN) for dynamic intrusion detection and response in cloud environments. The proposed methodology integrates a reinforcement learning-based decision-making mechanism with bidirectional traffic analysis to enhance the detection of both incoming and outgoing threats. By utilizing DQN, the system learns optimal defense policies over time, enabling proactive threat mitigation and intelligent adaptation to emerging attack vectors. Experimental evaluations using benchmark intrusion datasets demonstrate that the suggested framework significantly improves detection accuracy, decreases false alarm rates, and optimizes response strategies compared to conventional static detection methods. This study underscores the potential of deep reinforcement learning in developing intelligent, self-adaptive security solutions for next-generation cloud infrastructures.