Phishing Target Identification Based on Neural Networks Using Category Features and Images
Phishing attack, as a significant security concern in cyberspace, has continuously threatened organizations and Internet users. For organizations, the rise in the number of phishing target brands has instilled distrust and dissatisfaction in legitimate Internet users and even damaged brand equity. Therefore, more fine-grained phishing detection mechanisms are urgently needed. In this study, we propose PTI-NN, an effective model based on neural networks that uses category features and images to identify the target brands of phishing websites. We furthermore contribute a new dataset including 3,500 phishing websites and present thirty phishing category features, which facilitate pertinent phishing detection in the field of cyber security. In the proposed PTI-NN, an embedding-based DNN is constructed to process the category features, a 2D-CNN is constructed to process the images, and finally, a fully connected layer is used to predict the target brand of phishing websites. The experimental results show that our proposed model is able to classify seventy phishing-targeted brands with a high accuracy of 91.10%, which showcases the effectiveness of our method on the identification of phishing target brands.
Phishing attack, as one of the most prevalent types of cyber-attacks, evolves with the rapid development of the Internet and remains as a critical security threat. In recent years, the number of phishing attacks has been on the rise. As outlined in the report published by Antiphishing Working Group (APWG), the number of phishing attacks increased substantially through 2020, roughly doubling from 2019. After that, the amount of phishing rose consistently and steadily, reaching the highest level in APWG’s reporting history in December 2021. Moreover, organizations have increasingly been targets for phishing attackers that aim to steal personal property and valuable data by publishing fraudulent brand websites . According to the APWG, the number of brands targeted by phishing campaigns has trended upward since 2021, peaking at 715 in September 2021. The amount of targeted brands each month in 2021 that we collect from the APWG report is shown in Figure 1. The customers’ financial losses or data breaches inevitably affect organizations: it damages reputation and brand equity, meanwhile causes customer loss. Therefore, phishing detection mechanisms that more pertinently protect both organizations and individuals deserve to be of concern to security researchers.
Currently, techniques utilized for phishing detection are various, mainly classified as blacklists, heuristics, visual similarity, machine learning, and deep learning . In the latest studies, researchers proposed text-based approaches using NLP algorithms to defend against phishing attacks . These schemes have proven to be remarkably effective but have a common feature and a limitation that is the phishing websites’ identification problem is treated as a binary classification problem. In other words, most of the work is limited to the layer of classifying suspicious websites as phishing and nonphishing. In present circumstances flooded with counterfeit brand websites, the phishing detection strategies of this layer are obviously not precise enough to achieve the purpose of pertinent prevention. This motivates us to conduct a deeper-layer research on phishing websites’ identification problem.
PhishTank is a global online service that relied on crowdsourcing to collect phishing websites. Any Internet user can submit or verify phishing websites, which eventually are added to an ever-growing searchable public database . Due to the accessibility of the PhishTank database, it is widely used in phishing detection research studies. In the dataset file downloaded from PhishTank, all phishing websites have a “target” label indicating the target brand of the phishing attack. However, the “target” of most phishing websites, roughly 80 percent is labeled “other,” not a specific brand. This drawback goes against researchers trying to conduct more in-depth phishing analysis, such as the analysis of the distribution of target brands, the detailed statistical analysis of phishing websites targeting different brands (including the top-level domain (TLDs), the distribution of IP addresses, and the distribution of registrants), and the analysis of phishing trend. These analyses are meaningful for both organizations and individuals, contributing to the pertinent prevention of phishing attacks.
Some authoritative antiphishing organization, such as the Antiphishing Alliance of China (APAC), receives phishing reports daily and respond with the real phishing attacks as quickly as possible . Reviewers are normally required to identify the target brand of phishing websites, which is a large investment of time and energy. Moreover, brands targeted by phishing attackers are numerous and have the characteristics of globality, cross-language use, and cross-regional use. Owing to the limited knowledge, reviewers are not able to accurately classify phishing websites in other languages that target nonlocal brands. Therefore, phishing disposal requires multiparty collaboration across regions and industries. A unified data-sharing format and unobstructed data-sharing channels are particularly important in relation to the effectiveness of phishing disposal .
For researchers in organizations or the cyber-security field, having access to phishing data identified by brand enables them to reasonably design more pertinent phishing detection mechanisms. In addition, domestic and foreign standards for phishing data sharing and blockchain-based phishing data sharing, etc., explicitly require the provision of the brands being targeted. In real world scenarios, if browsers or security plug-ins are able to provide the target brands when informing the users that they are faced with phishing attacks, which is more persuasive to them.
In light of the above statements, the fine-grained classification of phishing websites, that is, identifying the target brands of phishing websites, is exceedingly crucial. In this study, we are focused on the target identification of phishing webpages by applying different learning algorithms and different kinds of features. In the implementation of learning algorithms, the dataset and the extraction of features from dataset are always pivotal . Therefore, first we construct a new dataset based on the phishing URLs of PhishTank, from which multiple category features are extracted. After that, we constructed an effective hybrid model based on neural networks, in which an embedding-based DNN processes’ category features and a 2D-CNN processes images. It turns out that up to seventy target brands can be identified with a high accuracy of 91.10%. The main contributions of our work can be summarized as follows:(i)To the best of our knowledge, we are the first to utilize the fusion of category features and images to achieve the purpose of identifying the target brands on phishing websites(ii)Due to the full consideration of phishing characteristics, thirty features are extracted from URLs, host information, web resources, and OCR results of phishing websites(iii)A dataset including phishing websites with their target brands, domain information, screenshots, icon pictures, and HTML contents is constructed and published for the cyber-security community (iv)A hybrid model named PTI-NN using category features and images is designed to classify the phishing brands, in which the E-DNN processes category features and the 2D-CNN processes images
2. Related Work
The related works are given in the following sections.
2.1. Phishing Target Identification
The existing studies on phishing target identification are mainly classified into two groups. One group of studies focuses on the methods of applying search engines. Wenyin et al.  queried keywords extracted from title information and page content on search engines and constructed a webgraph based on the websites contained in the search results. The website with the strongest parasitic relationship to the given website is regarded as the phishing target. Ramesh et al.  formed a set of keywords extracted from the title tag, meta tag, etc., and used Google search to discover a list of potential targets as the top results of the search. Yuan et al.  used the title, domain name of the URL, and web page links as query keywords for search engines to retrieve a number of phishing target candidates. Phishing target candidates that are most similar to the phishing website in title and text content are identified as the target of the phishing website. In these methods, only information from the URL and web page content is used, so that information (e.g., brand names) from embedded objects or images cannot be exploited.
To address this limitation, Marchal et al.  extracted three groups of keywords from the URL, page content, and OCR result of a web page screenshot and queried them on a search engine to select the most relevant phishing target. Peng et al.  explored the method of using a screenshot of the web page instead of the web page content. Features extracted from the screenshot with OCR technique are treated as keywords for Google search. As an extension, Van Dooremaal et al.  used features extracted from the DOM and the screenshots of a web page as search terms for search engines. However, the identification accuracy of the abovementioned methods depends on the retrieval results of the search engine. The repeated network connections also consume huge runtime overhead.
Another group of studies employ methods by using a target brand list. Fu et al.  first proposed the scheme of using a target brand list. They calculated a similarity value between the screenshot of a suspicious website and that of all websites in the target brand list and then obtained the phishing target if the similarity value was above a threshold. Likewise, Medvet et al.  utilized a signature which represent the information of text and images in the web page to compare a website with all websites in the target brand list. Since the logo represents the brand of a website, Afroz and Greenstadt  and Wang et al.  explored the methods that treat the logo as an invariant to compare. They attempt to locate logos on screenshots of websites and match them with the logos in a target brand list. Along this idea, the approach to locating logos has evolved and extended to the field of computer vision. In the latest work, Lin et al.  used object detection to locate the logos, and similarly, tried to determine the phishing target through list comparison. However, with a large number of brands and their different variants, target brand lists need to be constantly updated in real time. In addition, the logo is not the only clue for identifying the brand of a website, for there is also other useful information (e.g., brand names) in the content of the web page.
Considering the limitations of the studies described above, we prefer to explore a method for phishing target identification with a combination of feature/information extraction and the utilization of screenshots. Meanwhile, because learning algorithms have significant performance in feature and image processing, we also prefer the methods of applying them.
2.2. Limitations of PhishTank Dataset
PhishTank provides a visual platform for the public to identify and verify phishing websites. The detailed information about each entry of suspected phishing sites are shown on the platform, including: the submission number, URL, report date, and status; a screenshot or WHOIS metadata of the website, etc. Once reported and verified, the online phishing websites will be added to a downloadable database, which is available in multiple formats and updates hourly. The downloadable database records the phishing URLs that have been reported and verified by the public, the time of report and the time of verification, and the target brand or company of the website .
Through our observations, the public dataset downloaded from PhishTank has two main issues from our research perspective. On one hand, only online phishing sites remain in the database. Once a phishing website transitions from online to offline status, it will no longer exist in the database. For researchers who are interested in analyzing the features or mechanisms employed by historically live and active phishing websites, it is often impossible to do so. On the other hand, very few phishing sites are labeled with brands or companies, accounting for about twenty percent, and the rest are labeled as “Other.” This makes it difficult for researchers to analyze the distribution of phishing brands or the characteristics of phishing websites targeting the same brand or company.
This section presents our method for the detection of phishing websites. We first describe how the features are extracted from phishing websites under analysis and then introduce the proposed model for phishing target identification.
3.1. Feature Extraction
On the basis of feature definitions, we divide features into the following four categories: URL features, Host features, OCR features, and Web Resources’ features. Table 1 presents the details about each category of features.
3.1.1. URL Features
Generally speaking, a normal URL consists of protocol, domain name, file path, and query parameters and so does phishing URLs. However, these components on a phishing website always behave abnormally when compared with a legitimate website . Through our observation, phishing URLs attacking the same brand or company are sometimes very similar in component anomalies. Therefore, parsing the URL string itself and its components to acquire features is reliable in our work .
In the existing research on phishing attacks, information of phishing URL such as length and number of special symbols are often used as features [23–26]. As listed in Table 1, F1–F15 are the features related to the phishing URLs. Many phishing websites does not use HTTPS protocol but HTTP , thus F1 is used to denote the protocol of URL. Phishing websites always contain two or more subdomains in URL, the length of which is also quite long . Therefore, F2–F5 are used to denote domain, subdomains and the depth of domain level, and F6-F7 represent the length of different part in URL. Phishing attackers tend to use special characters commonly to separate the brand name from the prefix or suffix , meanwhile those targeting the same brand may use the same symbol or character frequently. Thus F8–F11 are used to denote the number of numbers and special characters (such as “@,” “//,” “-,” and “.“) in the URL and F12–F13 are used to denote the char or symbol appears most frequently in URL. Phishing URLs usually have multiple keywords (e.g., “secure,” “account,” “login,” “signin,” and “confirm”) , and those targeting the same brand probably have some common words that appear frequently (e.g., brand name). Therefore, F14 is used to denote the sensitive words in URL, and F15 is used to denote the top three words with the highest word frequency.
3.1.2. Host Features
WHOIS is an Internet record listing that provides domain information about registrars, registrants, registration dates, and so on. Due to the short life cycle of phishing websites, phishing websites generally have recent registration dates, near expiration dates, or recent update date, which are provided in WHOIS database . In addition, the Domain Name System (DNS) is the Internet’s registry, which stores a list of domain names along with their corresponding IP addresses . As commonly used DNS record type, an A-Record maps a domain name to the IP address and a CNAME Record stands for domain name aliases. Phishing attackers targeting a specific brand or company may register multiple phishing domains at the same location or use the same IP address to launch multiple phishing attacks. Therefore, querying WHOIS information and DNS records can provide many features.
Some details about host information such as domain age and registrant country are commonly used as features in research [27, 29]. As listed in Table 1, F16–F22 are the features related to WHOIS and DNS information. Domains of phishing websites targeting a specific brand may have the same valid days, which are probably registered by the same phishing attackers . Therefore, F16 is used to denote the valid days of domain. For privacy protection, registrants name are not provided in WHOIS database, so registrant country is an important information which provides the location of phishing attackers . F17 is used to denote the registrant country. However, for these two features are not sufficient to represent the full host information of the domain, we mined the information in the DNS record, which has not been used in the existing studies. In DNS record, A record provides IP addresses mapped from domains and CNAME record provides an alias of the domain . Through our investigation, we found that some domains of phishing websites targeting a specific brand use the same IP address or alias, which is also a clue to discovering the target. Therefore, F18–F21 are used to denote the IP addresses associated with domains, and F22 is used to denote the alias of domains. It should be noted that different IP address formats represent different host areas, thus we use different features to denote them.
3.1.3. Web Resources’ Features
The HTML source code of a website contains numerous HTML tags which determine how the text and images are structured and displayed in a web browser. Phishing attackers often abuse tags in HTML source code when creating phishing websites with minimal cost. Text in HTML source code often contains identity information (e.g., copyright information) related to a brand or company. Moreover, favicon is usually a unique identifying icon for brands or companies with legitimate websites. Treated as the symbol of a brand or company, favicon is generally an ICO file which contains a small image . Obviously, using favicons reasonably is an important entry point to spotting features of phishing websites . Therefore, useful features can be extracted from these elements in web sources.
As listed in Table 1, F23–F28 are the features related to HTML source codes and icon images. Phishing webpages tend to use external resources like scripts and images; meanwhile, forms are used to collect users’ important information . Therefore, F23 is used to denote the numbers of four tags including “link,” “script,” “img,” and “form. In phishing webpages, a number of sensitive words (i.e., “secure,” “account,” “login,” “signin,” and “confirm”) or brand names usually appear a number of times in the text of HTML, which are important clues to identify phishing targets. Thus, F24 and F25 are used to denote sensitive words and brand names extracted from the text of HTML. Some researchers use the TF-IDF algorithms to derive terms that mostly occur in the text of HTML, then query them as keywords in Google’s search engine . Inspired by this, F26 is used to denote the top three words which appear most frequently. Generally, letters in text of HTML are half-width. Through our investigation, some letters of brand words in text of HTML are replaced by full-width letters (i.e., facebook and f acebook) or special characters (i.e., amazon and mazon). For this, F27 is used to denote the number of these symbols (Unicode FF00–FFEF) in the text of HTML. As for the favicon, we converted it into a hex string, which can represent the image. As long as phishing websites targeting a specific brand have the same favicon, the value of hex string is the same. Thus, F30 is used to denote the hex string of the favicon.
3.1.4. OCR Features
3.2. Model Structure
In this study, a hybrid model is proposed for processing the extracted features and images, which mainly consists of embedding-based deep neural network (E-DNN) and 2D convolutional neural network (2D-CNN). Figure 2 shows the structure of the proposed model.
DNN has great feature expression and the ability to model complex mapping, for it extracts features layer by layer and combine low-level features to form high-level features . Typically, the connection of each layer in DNN is a combination of linear functions, which makes it difficult to handle sparse categorical features. In the context of neural networks, embedding is an effective way to transform those features from the original low-dimensional space to a high-dimensional space . Therefore, DNN jointly with embeddings are more robust and easier to do learning on the extracted features. Among the various areas of deep learning, CNN is usually quite promising at extracting deep features from high-dimensional input such as images . CNN efficiently and automatically learns the weights of the feature maps that consist of each layer, extracts abstract visual features such as points, lines, and faces from the input data, and preserves the relationships between pixels for the learning image. It has been proven to be very successful on image classification tasks [38–40].
3.2.1. E-DNN Layer
As depicted in Figure 2, E-DNN is applied to classify the extracted features. It consists of an embedding layer, a fully connected layer, and the tanh function exploited as the activation function. Let be the th sample’s input features, where D is the number of feature categories. We create embedding matrices , which indicate as an embedding layer to obtain the embedding of each feature, where is the number of feature value categories and is the dimension of the embedding. We then extract the input feature embedding from the following embedding matrices:where and is the concatenation of the corresponding embeddings of input features.
Then, is fed into a fully connected layer with active function tanh. Then, the output of this layer is calculated bywhere is the aggregated feature embedding of the th sample, is the activation function, is the parameter matrix of fully connected Layer 1, and it the bias of the layer.
3.2.2. 2D-CNN Layer
As depicted in Figure 2, 2D-CNN is applied to classify the screenshots of phishing webpages. It consists of a convolutional layer, a pooling layer, two fully connected layers, and the tanh function exploited as the activation function. Let be th sample’s input image. Before it is fed into the 2D-CNN layer, is reshaped into three-channel and two-dimensional (2D) matrices. Given a convolutional layer with multiple 2D convolution kernels, each output feature map of is produced by the convolution operation. Each element in the output two-dimensional convolutions is calculated as follows:where is the element of each output feature map, is the spatial position of the corresponding feature map, is the 2D convolution kernel with size , and b is the bias term.
With a pooling layer, a maxpooling operation is used to calculate the maximum value in a set of nearby inputs, which reduces total number of feature map’s dimensions . The maxpooling operation is expressed bywhere is the pooling of feature map value , is the size of the pooling window, and is the pooling stride .
Then, all the output feature maps of the pooling layer are flattened into a one-dimensional vector , which is fed into two fully connected layers with active function tanh to obtain the final embedding of 2D-CNN. The calculation is expressed bywhere indicates the final embedding of the image, and indicate the parameter matrix of the fully connected Layer 2 and Layer 3, and and is the bias term of each layer.
After obtaining the features from both E-DNN and 2D-CNN, we concatenate these two features into one feature vector as follows:where is the concatenation of the aggregated feature embedding and the image embedding which indicates the overall embedding of the sample .
Finally, we feed into the output layer with active function softmax:where is the parameter matrix of fully connected layer 4 and is the prediction results of our model, where is the number of classes and the -dimension of indicates the predicted probability of that sample belongs to class .
We conduct a cross entropy as the objective function:where is the number of samples, indicates our model, and ( is the predicted results of our model.
This section presents the procedure for dataset construction and execution of the experiment. Two groups of experiments are carried out to evaluate the effectiveness of the extracted features and the performance of the proposed model PTI-NN on our dataset. All the experiments are performed on a computer with AMD Ryzen 5 3600 6-Core, 3.60 GHz CPU, and 8 GB RAM. Meanwhile, experimental programs are written by the Python programming language.
4.1. Dataset Construction
To address the limitations of the PhishTank database, we constructed a dataset based on the phishing websites included in the PhishTank database. Figure 3 shows the procedure for dataset construction.
4.1.1. Data Collection
The data we collected include phishing URLs, WHOIS information, DNS information, screenshots, icon images, and HTML codes. Aiming to collect as many valid phishing samples as possible, we daily perform the following steps of data collection from October 2021 to June 2022:(i)Phishing URLs: we collect phishing websites that remained in the PhishTank database and obtain 27,403 unique URLs with submission numbers. Since our purpose is to categorize phishing brands, their corresponding targets are meanwhile transferred to our dataset.(ii)WHOIS information: we utilize WHOIS module to look up the detailed WHOIS information of phishing websites. Necessary information, including creation date, expiration date, and registrant country are stored in our dataset.(iii)DNS information: we parse the domain names of phishing websites to obtain DNS records through the dig command, then store the A-Records and CNAME Records of the domains in our dataset.(iv)Screenshots: through our observation, screenshots submitted by users on the PhishTank platform have issues with inaccurate content and inconsistent size. Having accurate and uniformly formatted screenshots is important for subsequent feature extraction and model training work. Therefore, we utilize Selenium module to automatically save the screenshots of phishing webpages in an identical size and type.(v)HTML source codes and icon images: we crawl files with an.ico extension from the host servers of phishing websites by utilizing Wget Command. The same as icon images, HTML codes are gathered by utilizing Wget Command.
4.1.2. Data Cleaning and Preprocessing
After the initial data collection, we observed that a large number of webpages were blank or not used for phishing any more. There are also some websites incorrectly associated with another brand or company. Phishing websites with these issues are considered invalid samples, which are not conducive to training effective models. So, we manually remove all the invalid phishing websites and ensure that each website is properly labeled with its corresponding brand or company. Specifically, we remove the invalid screenshots of samples and correct the labels of some samples, which ensures that the samples in our dataset could correspond to the correct target brands. As a consequence, 3,500 phishing samples are used in our experiments.
As described above, we have collected 3,500 phishing samples. As a matter of fact, the classifier will get into the problem of poor classification results when the number of samples with the same label is too small. To address this problem, we deleted the samples labeled with the same brand whose number is less than 5. For each group of experiments, we partitioned the dataset into a training set and a test set with a ratio of 8 : 2. Meanwhile, we took stratified random sampling for the samples of dataset according to the proportion of label categories and maintained this proportion in both the training set and the test set.
4.2. Model Implementation
All the models are implemented with PyTorch. For each model, we used Adam as an optimizer instead of the classical stochastic gradient descent procedure to update the network weights iteratively based on training data. The parameters of the proposed model are present in the descriptions given below.
In E-DNN, each feature is transformed into a vector of 16 dimensions in the embedding layer. All the vectors are then concatenated into a vector of size 1 × 960. This vector is then compressed into a vector of size 1 × 32 in the fully connected Layer 1. In 2D-CNN, 16 convolutional kernels of size 4 4 and stride of 4 is employed in the convolutional layer, through which each image matrix is converted into 16 matrices of size 16 × 16. The pooling layer conducts a maxpooling operation with a matrix of size 2 × 2 and a stride of 2 on the convolved 2D matrices and outputs 16 matrices of size 8 × 8. These matrices are then flattened into a vector of size 1 × 1024 which will pass through two fully connected layer. The sizes of vectors’ output from the fully connected Layer 2 and the fully connected Layer 3 are 1 × 512 and 1 × 128, respectively.The vector of size 1 × 32 output from E-DNN and the vector of size 1 × 128 output from 2D-CNN are concatenated into a vector of size 1 × 160. Finally, the output of fully connected Layer 4 has a size for the number of labels, which is the identification for phishing target detection. In addition, we set the learning rate to 0.1, the batch size to 50, the epoch number to 100, and the random state to 0, via comparative verification.
4.3. Evaluation Metrics
To evaluate the performance of models for multiclass classification task, accuracy, macro-F1 score, and weighted-F1 score are used as assessment indicators, which are widely applied in evaluation system of multiclass classifiers. These metrics rely on the four terms of true positive (TP), true negative (TN), false negative (FN), and false positive (FP). TP and TN representing the brands of phishing samples are correctly identified by the model, while FP and FN represent the opposite of TP and TN. Different metrics can be expressed as follows.
Accuracy is the ratio of the total number of correctly classified samples to the total number of samples:
For each class in the samples, precision is the ratio of the numbers of correctly classified samples to the numbers of all classified samples, and recall is the ratio of the number of correctly classified samples to the numbers of samples. F1 score is the harmonic mean between precision and recall:
Macro-F1 score and weighted-F1 score are variants of F1 score. Macro-F1 score calculates the average of F1 scores of all classes as follows:where is the class index and is the number of classes. On the basis of macro-F1 score, weighted-F1 score considers the problem of unbalanced distribution of class labels and performs a weighted average of F1 scores of all classes. The weight is determined based on the true distribution ratio of each class. The calculation of weighted-F1 score can be expressed bywhere is the class index and is the proportion of samples of class among all the samples.
4.4. Results and Analysis
4.4.1. Evaluation of the Extracted Features
In general, the features heavily affect the performance of classifiers, especially machine learning classifiers. From this perspective, we first employ seven different machine learning models to verify whether the extracted features are effective. The seven machine learning models are SVM (support vector machine), LR (logistic regression), K-Nearest Neighbor (KNN), DT (Decision Tree), RF (Random Forest), LightGBM (light gradient boosting machine), and XGBoost (extreme gradient boosting). Considering the unbalanced sample size of each brand, accuracy, and weighted-F1 score are recognized as the main evaluation metrics. From the results presented in Table 2, the extracted features perform best in LightGBM, which eventually classifies seventy phishing target brands with an accuracy of 0.8848 and a weighted-F1 score of 0.8769. Moreover, tree-based models perform better than other nontree-based models in this experiment. This can be explained by their ability to handle categorical data with robust algorithms.
For the purpose of training an optimal model suitable for detecting phishing target brand, it is needed to select an optimal feature set. We constructed different feature sets based on the class of features, and compared their performance on a multiclass classifier. LightGBM is chosen as the classifier because it performs best on all the extracted features. Comparing the results in Tables 2 and 3, it can be seen that the feature set including all the feature extractions leads to higher classification accuracy than the other feature sets in Table 3.
To analyze whether there are redundant features, we performed a feature correlation analysis. Correlation-based feature selection can effectively reduce feature dimensionality, especially for multilabel data with multiple features . The Kendall correlation coefficient is chosen for further feature correlation analysis because all the features are category features. We use the Kendall correlation coefficient to measure the correlation of each feature with the label and, meanwhile, to measure the correlation between the features. The results are shown in Figures 4 and 5, respectively. According to the result in Figure 5, the feature pairs with the values of Kendall correlation coefficient are greater than 0.8 are obtained. With the result of Figure 4, we removed five features that have the lowest correlation with the label. The retained twenty five features are fed into seven machine learning models, and the result is shown in Table 4. From the results of Tables 2 and 4, it is observed that the accuracy and weighted-F1 scores of the models using twenty five features were lower than those of the models using thirty features. Therefore, the thirty extracted features are verified as the optimal feature set.
4.4.2. Evaluation of Models
To obtain an optimal classifier that handles feature set, we evaluate the performance of deep neural network classifiers. Table 5 reports the results for DNN and embedding-based DNN (E-DNN) when the optimal feature set is used. From the results of Tables 2 and 5, we observe that E-DNN classifier outperforms the other classifier, including seven machine learning classifiers and DNN, having the highest accuracy of 0.8918 and weighted-F1 score of 0.8773. This is because embedding is an effective way to transform those features from the original low-dimensional space to a high-dimensional space, which reduce redundant information in the features.
The performance of E-DNN using only feature sets, 2D-CNN using only images, and the hybrid model PTI-NN using both feature sets and images are evaluated for obtaining an optimal model for this phishing target identification task. As the experimental results presented in Table 6, PTI-NN is the optimal model that achieves 91.10% accuracy. E-DNN and 2D-CNN also perform well, with an accuracy of 89.18% and 88.13%, respectively. This means the model that uses both feature set and images has a higher accuracy on this multiclass classification task than those models that only use feature data or image data. Therefore, the hybrid model is the optimal model in this study, which meanwhile showcases the effectiveness of our method on the phishing target identification task.
As brands are increasingly attacked by phishing groups, in this study, we propose a hybrid model named PTI-NN to identify the target brands of phishing websites. We construct a new dataset and overcome the limitations of the PhishTank database. We then use the features extracted from our dataset and the screenshots in the dataset as input to the models. In evaluation experiments, seven machine learning models are implemented. According to the experimental results, all the tree models and E-DNN which take the extracted features as input are able to classify seventy brands with high accuracy. Meanwhile, E-DNN outperformed machine learning models. Using both category features and images, PTI-NN, which consists of E-DNN and 2D-CNN, is the optimal model for this task.
In future work, we intend to adjust the model structure to promote the classification result. For example, parameter optimization techniques can be used to tune model parameters, and more efficient features or image processing models can be implemented.
The experimental dataset has been published in the open source community, and the link to access it can be found in reference .
Conflicts of Interest
The authors declared they have no conflicts of interest for this research.
Shihan Chen and Yixiang Lu contributed equally to this work.
This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 92067108, Natural Science Foundation of Guangdong Province under Grant No. 2022A050520013, and Macau Science and Technology Development Funds under Grant No. 0059/2021/AGJ.
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