In hostile environments, such as those found in biometric identification, network intrusion detection, and spam filtering, pattern classification algorithms are frequently utilized. In these kinds of environments, individuals may actively misrepresent data in order to endanger the functionality of the system. Traditional design techniques may produce an ineffective pattern classification system if adversarial circumstances are not taken into consideration during the design process. Exploiting these vulnerabilities has the potential to dramatically lower the efficacy of these systems, which in turn restricts their applicability to applications in the real world. The use of pattern categorization theory and design techniques to adversarial settings is a subject of study that has the potential to be fruitful but has received insufficient attention. To this day, there has not been any methodical research conducted on this subject. The purpose of this study is to throw light on a significant unresolved issue that affects the security evaluation of pattern classifiers during the design process. During the operational phase of these classifiers, when they may be exposed to a variety of risks, our primary focus is on analyzing the performance degradation that may occur. This evaluation will continue until the end of the running phase. In this study, we propose a comprehensive method for empirically measuring classifier security. This method was developed by the authors of this paper. Our plan is to define and then expand on the fundamental concepts that have been established by previous research. In addition, we demonstrate its applicability in three different and distinct situations that occur in the real world. The outcomes of the study indicate that the approach of security evaluation may provide a more comprehensive understanding of the behavior of classifiers in hostile contexts, which enables better design decisions to be made.