In today's world, computer networks have become vulnerable to numerous attacks. In both wireless and wired networks, one of the most common attacks is man-in-the-middle attacks, within which session hijacking, context confusion attacks have been the most attempted. A potential attacker may have enough time to launch an attack targeting these vulnerabilities (such as rerouting the target request to a malicious server or hijacking the traffic). A viable strategy to solve this problem is, by dynamically changing the system properties, configurations and create unique fingerprints to identify the source. However, the existing work of fingerprinting mainly focuses on lower-level properties (e.g IP address), and only these types of properties are restricted for mutation. We develop a novel system, called Verify-Pro, to provide server authentication using communication protocol dialects, that uses a client-server architecture based on network protocols for customizing the communication transactions. For each session, a particular sequence of handshakes will be used as dialects. So, given the context, with the establishment of a one-time username and password, we use the dialects as an authentication mechanism for each request (e.g get filename in FTP) throughout the session, which enforces continuous authentication. Specifically, we leverage a machine learning approach on both client and server machines to trigger a specific dialect that dynamically changes for each request. We implement a prototype of Verify-Pro and evaluate its practicality on standard communication protocols FTP, HTTP & internet of things protocol MQTT. Our experimental results show that by sending misleading information through message packets from an attacker at the application layer, it is possible for the recipient to identify if the sender is genuine or a spoofed one, with a negligible overhead of 0.536%.