# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 7 Conclusions  
This study presents a robust multi-layered phishing detection system capable of identifying conventional and Zero-Font phishing attacks in real time. By integrating machine learning with layered email security strategies, the system achieves high detection accuracy while maintaining a low false-positive rate, supporting practical deployment with minimal user disruption. Experimental results confirm the system's ability to mitigate risks such as data breaches and financial loss by accurately identifying subtle indicators of phishing behavior, including obfuscation techniques used in Zero-Font attacks. Automated email redirection further improves operational efficiency and protects users from engaging with malicious content. Despite these promising results, the absence of publicly available comprehensive datasets focused on Zero-Font phishing remains a key limitation. Future work should focus on collecting domain-specific datasets and investigating advanced machine learning \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Algorithm** & **Dataset** & **Accuracy** & **FP** & **FN** & **Features** & \
\begin{tabular}{c} **Real-Time** \\ **Detection** \\ \end{tabular} &
\begin{tabular}{c} **Zero-Font** \\ **Detection** \\ \end{tabular} \\ \hline \multirow{4}{*}{[16]} & Naive Bayes & \multicolumn{3}{c}{0.98} & \multirow{4}{*}{\(\times\)} \\ \cline{2-2} \cline{6-8}  & SVM & & 0.92 & & & & \\ \cline{2-2} \cline{6-8}  & Neural Networks & \multicolumn{3}{c}{N/A} & & & \\ \cline{2-2} \cline{6-8}  & K-nearest Neighbor & \multicolumn{3}{c}{N/A} & & & \\ \cline{2-2} \cline{6-8}  & Random Forest & \multicolumn{3}{c}{0.88} & \multirow{2}{*}{N/A} & \multirow{2}{*}{Body Analysis} & \multirow{2}{*}{N/A} & \multirow{2}{*}{\(\times\)} \\ \cline{2-2} \cline{6-8}  & Decision Tree & & & & & & \\ \hline
[17] & Naive Bayes & \multicolumn{3}{c}{Enron} & 0.96 & 0.02 & 0.04 & Header Analysis & N/A & \(\times\) \\ \hline
[18] & SVM & Ling-Spam & 0.91 & 0.05 & 0.07 & Content Analysis & N/A & \(\times\) \\ \hline
[19] & Random Forest & \multicolumn{3}{c}{SpamAsssin} & 0.97 & 0.03 & 0.02 & Hybrid Analysis & N/A & \(\times\) \\ \hline
[20] & Decision Tree & \multicolumn{3}{c}{PhishTank} & 0.94 & 0.04 & 0.06 & URL Analysis & ✓ & \(\times\) \\ \hline
[21] & Neural Networks & \multicolumn{3}{c}{Custom Dataset} & 0.99 & 0.01 & 0.02 &
\begin{tabular}{c} Multi-Feature \\ Analysis \\ \end{tabular} & ✓ & \(\times\) \\ \hline
[22] & CNN & \multicolumn{3}{c}{PhishingCo prus \& \multicolumn{3}{c}{SpamAsssin} & 0.99 & N/A & N/A & Body Analysis & N/A & \(\times\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Proposed \\ Method \\ \end{tabular} } & Decision Tree & & 0.96 & 19 & 15 & & & \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{Random Forest} & Cyber Cop. \& [PERSON] & & & & \\ \cline{2-2} \cline{6-8}  & [PERSON] and & 0.98 & 3 & 11 &
\begin{tabular}{c} Multi-Feature \\ Analysis \\ \end{tabular} & ✓ & ✓ \\ \cline{2-2} \cline{6-8}  & SVM & & 0.99 & 1 & 9 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of suggested solution and supervised learning algorithms. models, such as deep neural networks and hybrid architectures, to improve the detection capabilities. Furthermore, incorporating richer evaluation metrics, such as AUC-ROC and confusion matrix heatmaps, could offer deeper insights into model behavior, especially in class-imbalanced scenarios. In general, this research contributes to a practical and extensible framework for phishing detection. It underscores the importance of continuous innovation to address the evolving threat landscape in email-based cyberattacks. Z.A.A., R.Z.A., F.A.A. and J.M.A. : conceptualization, software, methodology, and writing--original draft preparation, N.A.S., S.A.A. and S.C.: supervision, writing--review and editing reviewing, supervision and formal analysis. D.A. and D.A.A.K. : methodology, and data curation, A.A.A. : resources,and funding acquisition. All authors have read and agreed to the published version of the manuscript.