# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 5 Proposed Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl. No.** & **Title** & **Authors** & **Year** & **Keynotes** \\ \hline \multirow{2}{*}{1} & A survey and classification of web phishing & \multirow{2}{*}{[PERSON] et al. [5]} & \multirow{2}{*}{2016} & \multicolumn{2}{c}{Presented a comprehensive analysis of phished website detection and outlining advantages and disadvantages} \\ \cline{3-5}  & detection schemes & & & & disadvantages \\ \hline \multirow{2}{*}{2} & Phishing Detection: Analysis of Visual Similarity & \multirow{2}{*}{[PERSON] et al. [13]} & \multirow{2}{*}{2017} & \multicolumn{2}{c}{Presented a comprehensive and comparative analysis of phishing attacks and detection using visual-similarity-based approaches} \\ \cline{3-5}  & Based Approaches & & & & \\ \hline \multirow{3}{*}{3} & Systematization of Knowledge (SoK): A & \multirow{3}{*}{[PERSON] et al. [29]} & \multirow{3}{*}{2017} & \multicolumn{2}{c}{Stystemized study of phishing-detection techniques and analysis.} \\ \cline{3-5}  & & Phishing Detection & & & \\ \hline \multirow{3}{*}{4} & SoK: A Comprehensive Reexamination of & \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{2020} & \multicolumn{2}{c}{Re-examined phishing research works, categorizing the existing works based on attack vectors and examination of properties and features for phishing detection} \\ \cline{3-5}  & Phishing Attacks Survey: Types, Vectors and & & & \\ \cline{3-5}  & Technical Approaches & & & \\ \hline \multirow{3}{*}{6} & Exquisite Analysis of Popular Machine & \multirow{3}{*}{[PERSON] et al. [6]} & \multirow{3}{*}{2021} & \multicolumn{2}{c}{Performed an exquisite analysis various machine-learning-based phishing-detection techniques, which includes analysis and taxonomy used in various methods} \\ \cline{3-5}  & Learning–Based Phishing Detection Techniques & & & \\ \cline{3-5}  & for Cyber Systems & & & \\ \hline \multirow{3}{*}{7} & Phishing Website Detection With Semantic & \multirow{3}{*}{[PERSON] et al. [31]} & \multirow{3}{*}{2022} & \multicolumn{2}{c}{Classified the ML-based detection techniques and performed an experiment based accuracy} \\ \cline{3-5}  & Features Based on Machine Learning Classifiers & & & & \\ \cline{3-5}  & A Comparative Study & & & \\ \hline \multirow{3}{*}{8} & A survey of phishing attack techniques, defence & \multirow{3}{*}{[PERSON] et al. [32]} & \multirow{3}{*}{2022} & \multicolumn{2}{c}{Phishing attack, highlighted the consequences of phishing threats and enlisted various challenges involved in phishing detection} \\ \cline{3-5}  & mechanisms and open research challenges & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of survey works on phishing detection. The proposed logo-detection mechanism uses this concept to extract the features and identify a logo. The novelty of the proposed mechanism is the use of hue value density and the average relative distance of each pixel with a given hue value from their centroid position as the features for detection. These features can uniquely represent any logo because the logos are generally designed by the combination of various colors and shapes in it. If coincidentally, two or more logo have the same combination of hue values and density, then it is the distribution of the pixels with the same hue value in the image that distinguish one logo from another. The overall work of detection of phishing logos involves a number of steps, such as logo image extraction from web-page or email, classifying the logo as one of the brand logo to confirm the email or web-page as phishing. A number of standard the logo extraction [16] and post detection blacklisting mechanism are already in use. Therefore, in this paper, we mainly focus on improving the precision of detection of the logo and pivot the attention for extracting the features of the logo image for classifying the phishing logo. The proposed phishing-detection mechanism have two phases as shown in Figure 5: (i) Training Phase and (ii) Detection Phase.