# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 7 Results  
The generated feature dataset was evaluated with two different types of data formation from the same set of images. The first dataset was generated by considering the each variant of logos of the same brand as separate class without grouping into a single class. The second dataset is generated by grouping all variant classes of the same brand into a single class. We considered five best performing multi-class classifiers. These were Decision Tree(D Tree), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB) and Ensemble Random Forest (ERF) multi-class classifiers [35; 36]. We varied the considered hue values from 6 to 15 clustered prominent values. For achieving the classification accuracy, we considered the randomized train-test split size to be 75:25 and the mean of the accuracy performed over 100 evaluation rounds.  
Figure 11 compares the detection accuracy of ML techniques without clubbing. The ERF multi-class classifier and D Tree were the best performer in every scenario clocking around average accuracy 85%, with peak accuracy of 87.21% for 8, 11 and above 13 prominent hue color values. This performance was marginally degraded with the reduction of the hue values. The D Tree classifier and KNN have marginally lower accuracy than ERF classifier. The SVM and GNB classifiers were unable to perform better for this dataset, where GNB multi-class classifier has the lowest accuracy. These three classifiers performed in a similar manner when ranging from 6 to 12 hue values and randomly deteriorated for values above the range.  
Figure 10: Number of distinct hue values per logo.  
Figure 9: Class-wise logo image distribution.  
Figure 12 shows the accuracy of the ML algorithms with clubbing. The accuracy of all the classifiers increased except for GNB. The ERF was still the best performer with a steady average accuracy of 86%. Moreover, the highest accuracy of our experiment is achieved by ERF with 88% for 7, 8 and 12 hue value counts. Surprisingly the performance of KNN was lower compared to no clubbing dataset, whereas the SVM accuracy has increased significantly for this dataset. The GNB remained as the least performer for both variants of the dataset.  
The overall accuracy comparison is provided in Figure 13. To summarize, the least performer was GNB with approximately 40% accuracy. The ERF multi-class classifier was the steady performer with approximately 87% accuracy on aggregate. This figure is equally comparable with that of the LogoSense detection mechanism by [PERSON] et al. [16], which has achieved 93.5% accuracy considering only an individual class of images for logo-based phishing detection.  
Figure 11: Accuracy without clubbing.  
Figure 12: Accuracy with clubbing.  
Table 4 compares the performance metrics and the accuracy of proposed work with logoSENSE [16]. logoSENSE used almost higher range parameters, such as dataset size and epochs, and achieved better accuracy compared to the proposed work. The parameters where the proposed work had an advantage over logoSENSE are the number brands and number of classes used for the experiment. In the proposed work, we used a higher variation of logo images for a single brand leading to 48 classes for a small dataset compared to logoSENSE, which used only 16 classes for a large dataset of size around 3800.