AI-Driven Predictive Cybersecurity Architecture for U.S. SDN-Controlled DWDM Datacenter Networks

The growing integration of Software-Defined Networking (SDN) and Dense Wavelength Division Multiplexing (DWDM) systems in modern U.S. datacenter infrastructures has created new cybersecurity threats, threats in speed and volume of network traffic, and networking threats of centralized control. Traditional rules-based security systems no longer provide real-time detection of advanced persistent threats, malware propagation, botnet intrusions, Distributed Denial of Service (DDoS) attacks and other forms of sophisticated attacks. In this regard, this study is aimed at proposing an AI-based predictive cybersecurity architecture for securing SDN controlled DWDM datacenter networks using intelligent threat prediction, anomaly detection and automated mitigation mechanisms to overcome these limitations. The proposed framework combines machine learning and deep learning with SDN-based traffic management to improve the visibility, scalability and adaptive cyber defense capabilities of networks. This study uses the CSE-CIC-IDS2018 data set which includes realistic enterprise network traffic and several attack categories for training and testing predictive models of cybersecurity. A number of AI algorithms are employed, such as Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze traffic behavior, classify malicious activities, and predict potential cyberattacks. The architecture is segmented into several layers are traffic monitoring to gather data, feature extraction to analyze the data, AI-based prediction to foresee potential threats, threat detection to identify them, and automated response to address them, all aimed at enhancing the security resilience of high-speed optical datacenter environments. Experiments should show that its detection accuracy is higher, it has fewer false positive rates, it can respond quicker to attacks and it can provide superior protection for the network than the traditional intrusion detection systems. This study will help build a next-generation intelligent cybersecurity framework that secures the DWDM datacenter in the United States from new cyber threats while enhancing the efficiency, scalability, and real-time adaptive security management of the system.