# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 4 Comparison and Analysis  
This section presents a comparative analysis of the phishing detection techniques reviewed in Section 3. The comparison focuses on key evaluation criteria relevant to designing a robust detection framework, including model performance, feature coverage, real-time detection capabilities, and support for emerging attack vectors such as Zero-Font phishing, see Tables 1-3. The comparison tables above synthesize a broad spectrum of phishing detection approaches across supervised, unsupervised, and hybrid techniques. They provide insights into the trade-offs between accuracy, real-time performance, and detection granularity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Algorithm** & **Dataset** & **Accuracy** & **FP** & **FN** & **Features** & **Real-Time Detection** & **Zero-Font Detection** \\ \hline \multirow{6}{*}{[16]} & Naive Bayes & & 0.98 & & & & & \\ \cline{2-9}  & SVM & & 0.92 & & & & & \\ \cline{2-9}  & Neural Networks & & N/A & & & & & \\ \cline{2-9}  & K-Nearest Neighbor & & 0.88 & N/A & N/A & Body Analysis & N/A & \(\times\) \\ \cline{2-9}  & Random Forest & & 0.92 & & & & & \\ \cline{2-9}  & Decision Tree & & 0.95 & & & & & \\ \hline
[17] & Naive Bayes & 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 & SpamAssssin & 0.97 & 0.03 & 0.02 & Hybrid Analysis & N/A & \(\times\) \\ \hline
[20] & Decision Tree & PhishTank & 0.94 & 0.04 & 0.06 & URL Analysis & ✓ & \(\times\) \\ \hline
[21] & Neural Networks & Custom Dataset & 0.99 & 0.01 & 0.02 & Multi-Feature Analysis & ✓ & \(\times\) \\ \hline
[22] & CNN &
\begin{tabular}{c} PhishingCorpus and \\ SpamAssssin \\ \end{tabular} & 0.99 & N/A & N/A & Body Analysis & N/A & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Supervised learning algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Algorithm** & **Dataset** & **Accuracy** & **FP** & **FN** & **Features** & **Real-Time Detection** & **Zero-Font Detection** \\ \hline \multirow{3}{*}{[23]} & Word Embedding & & & & & & \\ \cline{2-9}  & Novelty Detection & N/A & N/A & N/A & N/A & Email Headers & ✓ & \(\times\) \\ \cline{2-9}  & Rareness Selection & & & & & & \\ \hline \multirow{3}{*}{[24]} & Logistic Regression & & & & & & \\ \cline{2-9}  & Neural Networks & Kaggle & 0.93 & N/A & N/A & URL Analysis & ✓ & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Unsupervised learning algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Algorithm** & **Dataset** & **Accuracy** & **FP** & **FN** & **Features** & **Real-Time Detection** & **Zero-Font Detection** \\ \hline \multirow{3}{*}{[25]} & Creative Computing & & & & & & & \\ \cline{2-9}  & Fuzzy Logic & & 0.92 & & & & & \\ \cline{2-9}  & CNN & & N/A & & & & & \\ \cline{2-9}  & Keras Word Embedding & & & & & & \\ \hline \multirow{3}{*}{[26]} & Naive Bayes & & & & & & & \\ \cline{2-9}  & Naive Bayes & & & & & & \\ \cline{2-9}  & HMM & & & & & & \\ \hline \multirow{3}{*}{[28]} & SVM & & & & & & & \\ \cline{2-9}  & Perceptron & & & & & & \\ \cline{2-9}  & SGD & & & & & & \\ \hline
[29] & SVM & [PERSON] & 92\% & 1\% & N/A & Image Analysis & N/A & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Other techniques. Key considerations include dataset diversity, feature scope, and the presence or absence of support for novel obfuscation techniques such as Zero-Font phishing. Supervised learning approaches--particularly Random Forest, Decision Trees, and SVM--consistently demonstrate high accuracy and are widely adopted due to their robustness. Popular datasets such as Kaggle, Enron, and SpamAssassin serve as standard benchmarks in evaluating model performance. However, limitations persist. Most notably, no surveyed method addresses Zero-Font phishing, an increasingly prevalent and evasive tactic.