# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 1 Introduction  
Customer-oriented e-commerce solutions have brought forward a number of challenges in Cyber-Physical Systems. It is now common to conduct substantial transactions online for making basic purchases, internet banking, paying regular bills, acquiring a mortgage, vehicle loan, paying taxes and many more [1]. These online activities opens space for the hackers to exploit the vulnerabilities of the existing transaction service and launch cyberattacks in order to achieve financial gain or bring down the reputation of a person or organization. Among the list of common threats, phishing is a threat of major concern in IoT-based Cyber-Physical Systems [2, 3]. Phishing is a deception tactic that uses impersonated webpages, emails, calls or sms to extract classified data from individuals, such as their usernames, secret passwords, credit-card-related information and bank account details [4]. Phishing begins with a spoofed email, and then the victim is redirected towards the falsified websites to obtain the required information [5]. Phishing threats are widespread these days, and researchers are contributing various types of solutions for phishing detection. It is reported that fraudsters employ the optical characteristics stolen from genuine websites, particularly the logo, in their phishing websites in order to visually convince a victim to believe that he/she is using the genuine site. Moreover, they use a domain name close to the original domain with a minor mutation, such as facebook.com changed to facbook.com or facebook.com. In order to detect a phishing site, the first task is to conclude that it is visually similar to the genuine site with a major or minor inconsistency observed in the URL and other related parameters. The most important elements that make a fake webpage similar to the original one are logo images. Therefore, logo-based visual-similarity-based phishing detection approach lays the initial steps for an accurate and sophisticated phishing-detection technique. In a logo-based phishing detection approach, simple pixel-by-pixel matching is not effective to solve a phishing problem. This is because an attacker may slightly change a pixel's intensity to result in a lower matching score while not affecting the visual image quality. That is why feature extraction and selection is an important part of a logo-based detection approach. Several logo-based phishing-detection mechanisms have been reported in the literature utilizing a wide range of image features to compare and compute the similarity of a forged logo with the genuine ones. We inferred from the reported literature that most of the detection mechanisms work only with images of homogeneous dimension. In order to apply their classification and detection mechanism, the logo images require transformation. In this process, sometimes the actual characteristics of a logo are lost to a considerable extent. In this paper, we propose a logo-based phishing-detection mechanism to assess the identity consistency between the real and projected identities of a website using a hybrid technique including image-based similarity and machine learning (ML)-based approaches. The proposed mechanism uses the existence of unique set of hue values and their distribution in images irrespective of size to unambiguously identify the characteristics and use it to find the similarity between the logos. The advantage of the proposed mechanism is that it can detect and classify a logo image irrespective of its size. The contributions of this paper are as follows:  
* We propose a novel logo-identification mechanism for logo-based phishing detection. * The proposed mechanism uses the hue value ratio and the pixel density distribution in a logo that uniquely defines its feature and identifies well-known brands. * A number of 21 brands with 48 different classes of logos are used for training the ML model. * The detection accuracy of ensemble random forest algorithm is found to be the best with an accuracy of 87%.