Supporting TCP decisions in challenged networks via in-network telemetry Nowadays, networks are facing several challenges in developing solutions to solve intrinsic problems like achieving acceptable levels of latency, bandwidth, faults, and loss of packets that guarantee responsive applications and services together with a better user experience. TCP is still the most adopted transport protocol in network communication for its reliability, but it performs poorly in heterogeneous or wireless networks because it has no mechanism to distinguish between congestion and stochastic packet losses. Thus, the incoherence of the congestion window (cwnd) value could lead to performance and throughput degradation. For this reason, there is a recent attempt to let the TCP learn the best cwnd updates via Machine Learning (ML)-based approaches.