# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 13 Conclusions  
In this research, we have developed a prototype solution that integrates both phishing attack detection and end-user phishing education. The use of large language models, like GPT-3 and a Convolutional Neural Network Deep Learning Architecture, enables the prototype to detect email-based phishing attacks and allows phishing campaigns customized to specified business use cases to be generated for end-user training.  
By maximizing the defense effectiveness of phishing security at a granular level, AI and generative AI technologies could significantly reduce overall cyber risk. To comprehensively, we have developed a prototype for the future research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Batch Size** & **Accuracy** & **Precision** & **Recall** & **Fl Score** & **Confusion Matrix** & **Execution Time (s)** \\ \hline
32 & 0.94221 & 0.94875 & 0.95021 & 0.94948 & [65,290 & 4799] & \\  & & & & [4655 & 88,856] & \\ \hline
64 & 0.94795 & 0.97006 & 0.93788 & 0.95370 & [67,383 & 2706] & \\  & & & & & [5808 & 87,703] & \\ \hline
256 & 0.94602 & 0.96081 & 0.94407 & 0.95237 & [66,489 & 3600] & \\  & & & & [5230 & 88,281] & \\ \hline
**128** & **0.94857** & **0.96575** & **0.94348** & **0.95449** & **[66,961** & **3128**]** & \\  & & & & & [5285 & **88,226**] & **549.7330** \\ \hline \end{tabular}
\end{table}
Table 11: Performance Comparisons of Different Batch Sizes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Epochs** & **Accuracy** & **Precision** & **Recall** & **Fl Score** & **Confusion Matrix** & **Execution Time (s)** \\ \hline
5 & 0.94506 & 0.96583 & 0.93704 & 0.95121 & [66,989 & 3100] & 273.181 \\  & & & & & [5887 & 87,624] & \\ \hline
10 & 0.94857 & 0.96575 & 0.94348 & 0.95449 & [66,961 & 3128] & 549.7330 \\ \hline
13 & 0.94832 & 0.96463 & 0.94420 & 0.95431 & [66,852 & 3237] & 789.818 \\  & & & & & [5217 & 88,294] & 761.121 \\ \hline
15 & 0.94663 & 0.96721 & 0.93844 & 0.95261 & [67,114 & 2975] & 761.121 \\  & & & & & [5756 & 87,755] & \\ \hline
20 & 0.94711 & 0.96676 & 0.93978 & 0.95308 & [67,068 & 3021] & 996.013 \\  & & & & & [5631 & 87,880] & \\ \hline
**12** & **0.94908** & **0.96946** & **0.94055** & **0.95479** & **[67,319 & **2770**]** & **618.987** \\  & & & & & & [5559 & **87,952**] & **618.987** \\ \hline \end{tabular}
\end{table}
Table 12: Performance Comparisons over Different Epochs.  
sively mitigate increasingly sophisticated email phishing attacks therefore, businesses and their employees must both understand how cybercriminals may be using the technology, as well as embrace it as part of an evolving hybrid security framework.  
Conceptualization, P.K.K.L. and A.Z.Y.L. with oversight from Cyber Security Agency of Singapore; methodology, A.Z.Y.L.; software, A.Z.Y.L.; validation, A.Z.Y.L., P.K.K.L. and V.B.; formal analysis, A.Z.Y.L.; investigation, A.Z.Y.L.; resources, A.Z.Y.L.; data curation, A.Z.Y.L.; writing--original draft preparation, P.K.K.L.; writing--review and editing, P.K.K.L., A.Z.Y.L. and V.B.; visualization, P.K.K.L.; supervision, P.K.K.L.; project administration, P.K.K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Training datasets for phishing detection are extracted from public domain phishing link repositories detailed in Section 9 of this paper.  
We thank the Cyber Security Agency of Singapore for their guidance and advice during the development of the research prototype.  
The authors declare no conflict of interest.