Measuring entropy in a system represents the degree of uncertainty that characterizes the smooth, free and fair conduct of the network operations.  The change in quantum of entropy value raises an alarm of the unscrupulous behavior in the vicinity of the network. The continuous inspection of network characteristics and internet flow profiling maintains a constant vigil of the state, behavior and actions performed by the participating hosts in the network. The traffic flow from the multiple senders to either same/different receiver evinces a significant entropy escalation trend as the network composition at any timestamp is a rightful mixture of quality transmission attributes like source IP address, destination IP address, Sequence no.  This suffers a setback when the senders camouflaging as legitimate ones tries to fool the network administrators of the impending threat viz. DoS (Denial of Service) attack that the adversary may wish to coordinate via an idempotent HTTP Get Request operation. A request method is considered idempotent if the intended effect on the destination server with multiple identical requests is the same as the effect for a single such request. It produces the same result when executed over and over again. This ambiguous request operation directed from multiple/single sender to the intended receiver generates a broadcast storm that dampens the network services to the core. The ability of the idempotent nature is to generate as many genuine requests as possible and swamp the receiver with HTTP Get request packets. The receiver believes that the same host connection metric per flow count is generated by multiple senders but the reality is reverse.  The proposed solution to this problem is to aggregate and maintain a time stamp based and granular based flow attributes reserved for future entropy synchronization at several intermediate routers which will serve as evaluation checkpoints for the receiver. This Entropy based Deep Attention Mechanism (EDAM) coupled with DES (Deferred Entropy Synchronization) acts as a determinant for receiver to perform multi-level cross verification at different time instants and perform deferred synchronization with the reserved values. The performance of this deep attention based entropy synchronization approach witness a deep spike in prediction accuracy and this is plotted with no. of idempotent attackers in the x axis and the improved accuracy in Y axis.