## 5 Results
### Additional Analysis  
Based on these results, we were able to establish additional correlations between various attacks at different stages and individuals responding to one or more attacks. Table 17 monitors the evolution of employee vulnerability across the three simulations within a uniform participant group:  
The data suggests a troubling trend: employee resilience declines following successive assaults.  
Table 18 illustrates the relationship between educational engagement and phishing resilience:  
Further analysis was performed to investigate the cumulative impact of multiple phishing simulations compared to a mixed-method strategy that incorporated instruction. Table 17 delineates the progression of vulnerability among participants who engaged in  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **The First Attack** & **The Second Attack** & **The Third Attack** \\  & **Simulation** & **Simulation** \\ \hline Number of vulnerable persons & & \\ undergoing three phishing & & \\ simulations & & \\ \hline \hline \end{tabular}
\end{table}
Table 17: The number of people who have become victims of the third phishing attack after the first and second attack simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **The First Attack** & **The Third Attack** \\  & **Simulation** & **Simulation** \\ \hline Number of vulnerable people & & \\ undergoing education and & & \\ two simulations & & \\ \hline \hline \end{tabular}
\end{table}
Table 18: The number of people who have become victims of the third phishing attack after the first simulation and attended education on defense against phishing attacks.  
all three scenarios. The substantial rise in the number of compromised persons during the third attack (from 1 to 39) indicates a marked deterioration in resilience over time.  
Table 18 contrasts this with users who, after the initial phishing simulation, participated in formal phishing awareness education and then faced a third attack. Notwithstanding an early compromise in one instance, the total of compromised users in the third attack was 30, which is fewer than the 39 recorded in the simulation-only cohort.  
A chi-square test for independence was conducted to determine if this difference was statistically significant. The outcome (\(\chi^{2}(1,\) N = 237) = 2.57, \(p\) = 0.109) demonstrates no statistically significant disparity between the two paths. Nonetheless, the outcome suggests a possible trend supporting educational intervention. That indicates that although neither method offers complete protection, structured education may provide some improvement in long-term resistance to phishing compared to simulations alone.  
Table 18 underscores a troubling implication: of the 30 employees who underwent phishing awareness training and engaged in all three simulations, all 30 ultimately failed the third simulation. That raises doubts about the sustainability and effectiveness of a singular educational effort. Although training may enhance immediate awareness of phishing strategies, its efficacy appears to wane over time, particularly during high-pressure or low-alert periods (e.g., holidays). That also suggests a possible overestimation of training retention or a disconnect between the training material and the evolving strategies of threats.  
These findings indicate the requirement for  
* Increased frequency and adaptable training cycles, potentially incorporating just-in-time refreshers.
* Dynamic simulation content that updates to represent emerging danger vectors.
* Incorporation of behavioral analytics to customize training intensity and frequency.  
Both tables demonstrate that static methodologies are inadequate. Organizations should implement continuous, behavior-aware, and adaptive defensive techniques to maintain high levels of end-user alertness and resilience.