  Packet losses in the network significantly impact network performance. Most
TCP variants reduce the transmission rate when detecting packet losses,
assuming network congestion, resulting in lower throughput and affecting
bandwidth-intensive applications like immersive applications. However, not all
packet losses are due to congestion; some occur due to wireless link issues,
which we refer to as non-congestive packet losses. In today's hybrid Internet,
packets of a single flow may traverse wired and wireless segments of a network
to reach their destination. TCP should not react to non-congestive packet
losses the same way as it does to congestive losses. However, TCP currently can
not differentiate between these types of packet losses and lowers its
transmission rate irrespective of packet loss type, resulting in lower
throughput for wireless clients. To address this challenge, we use machine
learning techniques to distinguish between these types of packet losses at end
hosts, utilizing easily available features at the host. Our results demonstrate
that Random Forest and K-Nearest Neighbor classifiers perform better in
predicting the type of packet loss, offering a promising solution to enhance
network performance.
