ABSTRACT
As cyber threats become more sophisticated, the need for advanced security measures grows exponentially. This research explores the integration of dynamic malware analysis into Software Defined Networks (SDNs), leveraging the programmable nature of SDN architectures to enhance network security. The study focuses on the incorporation of machine learning techniques for automated detection and response to malware threats within SDNs. Through the creation of isolated environments and intelligent flow control, SDNs facilitate dynamic analysis, preventing the spread of malware and enabling efficient monitoring. Machine learning algorithms, deployed for behavioral analysis and anomaly detection, scrutinize the actions of malicious software during execution. The integration of threat intelligence feeds with SDN controllers enables rapid response to emerging threats. Automated responses, orchestrated by SDN controllers, dynamically adjust network policies based on analysis results, mitigating the impact of malware. This approach allows for continuous learning, with machine learning models updated regularly to adapt to evolving threats. The proposed framework offers a responsive and adaptive security infrastructure, fostering real-time detection and mitigation of malware in SDN environments.
