# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 4 Literature Survey
They claim to use a unique combination of features to train and detect phishing attack using ML algorithms. The important point is they used only the URL to extract all the features for phishing detection. [PERSON] et al. (2019) provided an anti-phishing solution for real-time environments. They used a small number of features (nine) to achieve a higher detection accuracy, which makes the technique suitable for resource-constrained devices. They used four ML-based classifiers to train and test the selected features and claimed to have nearly 99% accurary. [PERSON] et al. (2019) combined three correlation methods: Spearman, Power Predictive Score (PPS) and Maximal Information Coefficient (MICe) for feature selection and recursive feature elimination method to minimize the number of features to select only the use features for phishing detection without compromising the accuracy. They used a number of ML algorithms to verify the accuracy of selected features under different scenarios. [PERSON] et al. (2019) proposed a mechanism called PhishIntention, which extracts the intention of phishing in a web-page by visually extracting brand intention and interacts with the web-page to gather its intention regarding phishing. They extracted the abstract web-page layout and determined the phishing intention from it. They used a heterogeneous system of a deep-learning-vision-model to implement their work. The summary of the phishing-detection techniques is tabulated in Table 1. The list of review articles referenced for our study and analysis of phishing-detection techniques are provided in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl. No.** & **Title** & **Authors** & **Year** & **Keynotes** \\ \hline \multirow{2}{*}{1} & Utilization of website logo for phishing detection & [PERSON] et al. (2019) & \multirow{2}{*}{2015} & Content-based image retrieval feature from Google image database and used image similarity for phishing detection \\ \cline{2-4}  & Deep learning for logo recognition & [PERSON] et al. (2019) & & \multirow{2}{*}{2017} & CNN-based Deep Learning model for logo detection \\ \cline{2-4}  & Detecting Phishing Attacks Using NLP and ML & [PERSON] et al. (2019) & & & NLP-based approach to analyse text and content of webpages to detect phishing attack. \\ \hline \multirow{2}{*}{4} & Deep Learning for Phishing Detection & [PERSON] et al. (2019) & & \multirow{2}{*}{2018} & Used both URL and logo to propose a phishing-detection mechanism \\ \cline{2-4}  & Jail-Phish: An improved search-engine-based & & & & \\ \cline{2-4}  & phishing detection system & [PERSON] and [PERSON] (2019) & & & \\ \hline \multirow{2}{*}{6} & A keyword-based combination approach for detecting phishing webpages & [PERSON] et al. (2019) & & \multirow{2}{*}{2019} & A compound approach of search engine, heuristics rules and LR classifier is used to detect a phishing site \\ \cline{2-4}  & LogoSENSE: A companion HOG-based logo & [PERSON] and [PERSON] (2019) & & & \\ \cline{1-1} \cline{2-4}  & detection scheme for phishing brand recognition & [PERSON] and [PERSON] (2019) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of phishing-detection techniques.