The landscape of malware development is ever-changing, creating a constant catch-up contest between the defenders and the adversaries. One of the methodologies that has the potential to pose a significant threat to systems is malware evasion. This is where malware tries to determine whether it is run in a controlled environment, such as a sandbox. Similarly, a malware can also learn how an Anti-Malware System (AMS) decides whether an input program is a malware or in fact benign with the goal of bypassing it. On the other hand, the AMS tries to detect whether a malware sample is performing such evasive checks, e.g. by evaluating the results of Reverse-Turing Test (RTT). This learning process can be viewed as a ‘battle’ between the AMS and the malware, due to the malware attempting to defeat the AMS, where a successful win for the malware would be to evade detection by the AMS and, conversely, a win for the AMS would be to correctly detect the malware and its evasive actions. We propose a visualisation-based system, called Gotta Evade ‘em All, that allows cyber-security analysts to clearly see the evasive and anti-evasive actions performed by the malware and the AMS during the battle.