# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 4 Literature Survey  
A huge number of contributions have been made in the field of detection mechanisms in each category of phishing detection. The major ones are the URL-scan-based approach along with the use of ML algorithms. In this section, we cover a few of the recent works of all the categories as reported in the literature. [PERSON] et al. [6] inferred that the hybrid form or combination of multiple phishing-detection techniques outperform traditional approaches in terms of accuracy. Despite computational overhead, ML-based approaches achieve better accuracy and lower false positive rates. [PERSON] et al. [17] used basic color characteristics to represent a logo image and verified the identity consistency between the true and the projected identity of a website to detect phishing. [PERSON] et al. [5] analyzed various novel strategies and significant work proposed in the area of phished website detection and depicted their respective flaws and virtue. [PERSON] et al. [18] proposed a phishing-detection technique using deep-learning model. They first utilized a logo region proposal for logo identification followed by applying Convolutional Neural Network (CNN) for logo classification. They claimed to achieve 96% accuracy with their proposed model. [PERSON] et al. [19] proposed a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based method for small-scale logo recognition along with URL-based code for phishing attack detection. [PERSON] et al. [20] proposed a method that focuses on the attack's natural language text and semantic analysis of the text using Natural Language Processing (NLP) techniques to detect incorrect remarks that are plausible for a phishing attack. [PERSON] et al. [21] proposed a compound technique consist of keyword search, applying heuristics, followed by using a logistic regression classifier to identify phishing URL. First, a keyword search is performed for the suspicious page in the baidu search engine and the top ten domains are matched. If matching fails, then the heuristics are used to check for genuineness of the page. If the page is still not categorized as genuine, it is fed to a logical regression classifier to classify it as a phishing site or as genuine. [PERSON] and [PERSON] [22] proposed a detection mechanism called Jail-Phish. Jail-Phish aims to improve the detection accuracy of search engine for detecting a phishing site on a compromised server. The detection procedure includes the comparison of the suspicious site and the genuine sites with matched domain. The matching results in a similarity score between the suspicious site and the domain, and this score is used to detect the phishing page. [PERSON] and [PERSON] [16] proposed LogoSENSE, which is a Histogram Orient Gradient (HOG)-based logo detection method for phishing web page recognition. They used HOG to obtain visual representations of target brand logos in a scale invariant fashion. They  
Figure 3: Basic-logo-based phishing web-page detection. used max-margin loss-equipped Support Vector Machine (SVM) classifier for classification of a phishing logo. [PERSON] et al. (2019) provided an automated white-list technique for identifying phishing attacks. Here, the visible link and the actual link are analyzed to determine the white-list. The similarities of the known legitimate site are calculated by binding the domain name with the contents of the whitelist. Next, these are matched with the IP address to obtain the final similarity score. This information helps to decide the membership of a hyperlink in the whitelist. The authors claimed to achieve an average accuracy of 96.17%. [PERSON] et al. (2019) proposed a hybrid deep-learning system, Phishpedia, which addresses identity logo recognition and matching logo variants to detect phishing logos. This method provides better accuracy in phishing logo detection but is prone to false-positives in many scenarios. [PERSON] et al. (2019) proposed a light-weight URL-based phishing-detection mechanism.