ashwinn-v/Traffic-classifier-SDN

This project is a classic academic/hobbyist exploration of Machine Learning applied to SDN. With 44 stars and no activity in over 4 years, it represents a 'frozen' prototype rather than a living tool. The defensibility is minimal because it relies on standard Scikit-learn algorithms (Random Forest, KNN) applied to simulated data from D-ITG, which is a common pattern in networking theses but lacks the robustness required for production environments. In the competitive landscape, this project is overshadowed by both commercial solutions (Cisco's Encrypted Traffic Analytics, Darktrace) and modern open-source networking observability stacks like Cilium (which uses eBPF for much higher performance and deeper kernel-level insights). Frontier labs (OpenAI/Anthropic) are unlikely to compete here as this is a niche networking infrastructure problem, but the 'Platform Domination Risk' is medium because networking incumbents (Arista, VMware, Cisco) have already integrated more sophisticated versions of this functionality directly into their NOS (Network Operating Systems) and hardware ASICs. The project serves as a useful educational reference for how to hook ML into an OVS-based controller but offers no unique moat or technical breakthrough.