Net Defender: Enhancing SDN Security with Deep Learning Against BOTNET Threats

Computer network defenses are particularly vulnerable to botnet attacks when it comes to Software-Defined Networks (SDNs). The purpose of this research is to examine the use of Deep Learning Techniques inside software-defined networks (SDNs) to detect and block assaults of this kind. Predetermined signatures or heuristic criteria are commonly used by existing botnet detection systems, which limits their ability to adapt to constantly evolving attack strategies. Furthermore, situations involving changing network circumstances may not be amenable to the scalability and efficiency of conventional mitigation solutions. SDN-DL combines the programmability and centralized control of software-defined networks (SDNs) with deep learning's powerful pattern recognition capabilities. Software-defined networking (SDN) controllers that use Deep Learning models enable real-time monitoring of network traffic patterns. This makes it possible to proactively find botnet activities and stop them. The use of SDN-DL enables the support of dynamic network adaptation and response, which lessens the window of vulnerability to botnet attacks and limits the amount of false positives. Software-Defined Network security has been enhanced due to the method's success in accurately detecting and countering botnet attacks. Extensive testing and modeling have shown this.