Fighting Phishing with Discriminative Keypoint Features of Webpages

Kuan-Ta Chen, Jau-Yuan Chen, Chun-Rong Huang, and Chu-Song Chen

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Abstract

Phishing is a form of online identity theft associated with both social engineering and technical subterfuge. As such, it has become a major threat to information security and personal privacy. According to Gartner Inc., in 2007, more than $3.2 billion was lost due to phishing attacks in the US, and 3.6 million people lost money in such attacks. In this article, we present an effective image-based anti-phishing scheme based on discriminative keypoint features in webpages. We use an invariant content descriptor, the Contrast Context Histogram (CCH), to compute the similarity degree between suspicious pages and authentic pages. The results show that the proposed scheme achieves high accuracy and low error rates.

1  Introduction

Phishing is a form of online identity theft associated with both social engineering and technical subterfuge. Specifically, phishers attempt to trick Internet users into revealing sensitive or private information, such as their bank account and credit card numbers. Unwary users are often lured to browse counterfeit websites through spoofed emails, and they may easily be convinced that fake pages with hijacked brand names are authentic. When users unwittingly browse phishing pages, phishers can plant crimeware, also known as malware, on the victims' computers. Then, through the crimeware, phishers can steal users' private information, redirect users to malicious sites directly, or redirect them to the intended websites by way of phisher-controlled proxies.
The Anti-Phishing Working Group (APWG) reported that the number of phishing webpages has increased by 28% each month since July 2004, and 5% of users who receive phishing emails respond to such scams. More than 66,000 cases of phishing were reported to, or detected by, APWG in September 2007; and up to 95% of the phishing targets were related to financial services and Internet retailers. According to a survey by Gartner, Inc., in 2007, more than $3.2 billion was lost due to phishing attacks in the United States, and 3.6 million people lost money in such attacks. Phishing has thus become a serious threat to information security and the privacy of Internet users.
eBay-login.jpg (a)
eBay-fake1.jpg (b)
eBay-fake2.jpg (c)
Figure 1: Comparison of the official eBay page and phishing pages: (a) the official page; (b) a phishing page with the modified logo; (c) a phishing page with an advertisement banner inserted.
To deceive users into thinking phishing sites are legitimate, fake pages are often designed to look almost the same as the official pages in both layout and content. In addition, an arbitrary advertisement banner may be inserted to redirect users to another malicious website if they click on it. Take the phisher's favorite target, eBay, for example. Figure 1(a) shows the login page of the official eBay website, while Fig. 1(b) is a phishing page with a slight modification to the logo; specifically, the logo is smaller and the colored bar below the logo is missing. Figure 1(c) is a phishing page with an advertisement banner placed at the top of the page. These examples show how phishers ensnare the public and how difficult it is for general users to distinguish between legitimate and phishing pages.

2  Current Anti-phishing Techniques

Several anti-phishing techniques have been proposed in recent years to strive to counter or prevent the increasing number of phishing attacks. Generally speaking, phishing detection and prevention techniques can be divided into two categories: 1) e-mail level approaches, including authentication and content filtering; and 2) browser integrated tools, which usually use URL blacklists, or employ webpage content analysis.
E-mail filtering techniques used to prevent phishing are quite popular in anti-spam solutions, as both try to stop email scams from reaching target users by analyzing the content of emails. The challenge in designing such techniques lies in how to construct efficient filter rules and simultaneously reduce the probability of false alarms. Phishing messages are usually sent as spoofed emails; therefore, a number of path-based verification methods have been proposed. Current mechanisms, such as Sender ID proposed by Microsoft and DomainKey developed by Yahoo, are designed by looking up mail sources in DNS tables. However, these solutions have not been widely applied yet. Currently, the companies only provide the mechanisms in their own products and services free of charge.
A browser-integrated tool usually relies on a blacklist, which contains the URLs of malicious sites, to determine whether a URL corresponds to a phishing page or not. In Microsoft Internet Explorer 7, for example, the address bar turns red when a malicious page is loaded. The effectiveness of a blacklist is strongly influenced by its coverage, credibility, and update frequency. At present, the most well-known blacklists are those maintained by Google and Microsoft, which are used by the most popular browsers, Mozilla Firefox and Microsoft Internet Explorer, respectively. However, experiments [10,4] show that neither database can achieve a correct detection rate over 90%, and the worst case scenario can be lower than 60%. Some browser-integrated tools, e.g., SpoofGuard [2], iTrustPage [11], and Liu et al. [8,12] adopt approaches other than blacklists. One of these approaches examines the URL of a suspect page to determine if it is a spoofed address. For example, http://fake.net/www.amazon.com/sign-in may link to a phishing page that mimics http://www.amazon.com/sign-in as the target. Some other approaches focus on analyzing a webpage's content, such as the HTML code, text, input fields, forms, links, and images.
In the past, the content-based approach, which analyzes the HTML code and text on a webpage, proved effective in detecting phishing pages; however, phishers responded by compiling phishing pages with non-HTML components, such as images, Flash objects, and Java applets. For example, a phisher may design a fake page which is composed entirely of images, even if the original page only contains text information. In this case, the suspect page becomes unanalyzable by content-based anti-phishing tools as its HTML code contains nothing but HTML <img/> elements. To address this problem, Fu et al. [3] proposed detecting phishing pages based on the similarity between the phishing and authentic pages at the visual appearance level, instead of rather than using text-based analysis. However, the proposed approach is susceptible to significant changes in the webpage's aspect ratio and colors used.

3  The Proposed Scheme

As phishers may compose visually similar phishing pages in many different ways with non-text HTML elements, such as images and Flash objects, we compute the similarity of the phishing pages and the authentic pages at their presentation level. Specifically, we treat phishing page detection as an image matching problem. Figure 2 illustrates the flow of our proposed detection scheme, which involves two steps: 1) image-based page matching, and 2) page classification.
flow.png
Figure 2: The flow of the proposed phishing detection scheme. We first take a snapshot of a suspect page, and extract its keypoint feature information. Next, the features are matched with the keypoint feature information of protected webpages. The suspect page can then be assessed to determine whether or not it is a phishing page.
In the proposed scheme, we first take a snapshot of a suspect webpage and treat it as an image in the remainder of the detection process. We use the Contrast Context Histogram (CCH) descriptors proposed by Huang et al. [6,7] to capture the invariant information around discriminative keypoints on the suspect page. The descriptors are then matched with those of the authentic pages of the protected domains, which are stored in a database compiled by users and authoritative organizations, such as the Anti-Phishing Working Group (APWG). The matching of CCH descriptors yields a similarity degree for a suspect page and an authentic page. Finally, we use the similarity degree between two pages to determine whether the suspect page is a counterfeit or not. If the similarity degree between a phishing page and an authentic page is greater than a certain threshold, the suspect page is considered as a phishing page of the authentic page, and considered genuine if it is not a phishing page of any of the authentic pages in the database.

3.1  Contrast Context Histogram (CCH)

Image matching techniques have long been used for a long time in the computer vision and image processing fields. To determine whether two images are similar, a common approach involves extracting a vector of salient features from each image, and computing the distance between the vectors, which is then taken as the degree of visual difference between the two images.
The color histogram, which represents the distribution of the colors used in an image, for example, is one of the most widely-used features for image matching. However, we consider it unsuitable for computing the similarity between webpages. The reason is that webpages usually contain fewer colors than paintings; thus, it is not uncommon to find that many webpages have similar color distributions. In other words, the color histogram is not a useful discriminative feature for judging the similarity of webpages.
We use the Contrast Context Histogram (CCH) [6,7] descriptor because of its effectiveness and computational efficiency. Originally, the CCH descriptor was designed to achieve scale- and rotation-invariance in image matching; that is, two images are considered similar even if one of them has been undergone various types of scale- or rotation-transformation. However, such transformations are rarely seen in phishing pages because the pages must be very similar to the corresponding authentic pages in order to deceive unsuspecting users. Thus, we adapt the CCH descriptor to a more light-weight design for webpage comparisons. We call our design the L-CCH descriptor hereafter.
cch_keypoint.png
Figure 3: (Left) Keypoints (marked by green crosses) detected in the image. Keypoints are the points in an image that can still be detected easily after changes (e.g., lighting variations) are applied. (Right) The log-polar coordinate system centered on a keypoint. The angle coordinate is divided into 8 levels, and the distance coordinate is divided into 3 levels; we have n=24 sub-regions as a result.
To construct L-CCH descriptors for an image, we only use the gray-level information, which we obtain by averaging the red, green, and blue values of each pixel in the image. The Harris-Laplacian corners are then taken as the keypoints of the image. Readers not familiar with the Harris-Laplacian corner may refer to Mikolajczyk and Schmid's work [9] for details. Basically, the corner-detection method finds a number of salient points in an image. A point is considered a keypoint if it can still be detected after the image undergoes various changes, such as shifting, lighting variation, color transformation, or format conversion. Fig. 3 shows an example of the keypoints detected (marked by the green crosses) in an image.
cch_diff.png (a)
contract_region.png (b)
Figure 4: The L-CCH descriptor with the log-polar coordinate system. (a) The gray-value contrast value between neighboring pixels and the keypoint (the center). (b) The L-CCH descriptor with a 2-tuple contrast vector in each sub-region.
We use the relative brightness of neighboring pixels to describe a keypoint. By uniformly quantizing the azimuth angle and the distance coordinates, the neighbor region of each keypoint is divided into n non-overlapping sub-regions, where n=24 in Fig. 4. The advantage of using a log-polar coordinate system is that this system is more sensitive to the image points nearby the center than those points farther away. For each neighboring pixel of a keypoint, we calculate the contrast value, i.e., the difference between the gray levels of the pixel and those of the keypoint. As shown in Fig. 4(a), a sub-region may contain some pixels with positive contrast values (the pink pixels), and some with negative contrast values (the blue pixels). We summarize the information in each sub-region by averaging the positive and negative contrast values respectively; therefore each sub-region can be described by a 2-tuple contrast vector, as shown in Fig. 4(b). We then concatenate the contrast vectors of all sub-regions to form a 2n-dimensional vector and define it as the L-CCH descriptor, where n is the number of sub-regions. Finally, to make the L-CCH descriptor invariant to linear lighting changes, we normalize it to a unit-length vector.
Having obtained the L-CCH descriptor for each keypoint, we can quantify the similarity between two keypoints based on the Euclidean distance between their descriptors. A short Euclidean distance indicates that the keypoints are similar in terms of neighboring information. Based on this property, we find the most similar keypoint on a suspect webpage for each keypoint, K, on the authentic webpage by the following steps: First, we search for the two keypoints, A and B, on the suspect page that have the shortest and the second-shortest Euclidean distances from the keypoint, K, on the authentic page. Second, we consider K and A as a successful match if the ratio between the distance from K to A and the distance from K to B is smaller than a certain threshold (set to 0.6 in our experiments); otherwise, we consider that the keypoint K has no corresponding keypoints on the suspect page. An example of image correspondence found by the L-CCH descriptor is shown in Fig. 5, where a line connecting two keypoints means that a match exists between the images.
cch_sample.png
Figure 5: Sample result of image matching using the L-CCH descriptor.

3.2  Page Similarity Degree

To determine whether a suspect webpage is a phishing webpage, we evaluate its similarity to the potential target based on CCH descriptors. Ideally, the number of successful matches found by descriptors should indicate the degree of similarity between the two pages. However, this is not always true in the cases of webpage comparison. Two webpages may have a number of keypoint matches not because they look similar, but simply because they contain the same logo, e.g., the logo of VeriSign, Inc., a well-known identity protection service provider. Therefore, to judge the similarity of two webpages, we need to consider not only the number, but also the spatial distribution, i.e., the locations, of the matched keypoints.
ebay_banner_cluster.png
Figure 6: Clustering and matching of eBay's official page and a phishing page. Different clusters are circled in different colors.
ebay_Paypal.png
Figure 7: Matching two pages from different sites. In this case, there are too few matched keypoints required to perform clustering.
To take the location of matched keypoints into account, we use the k-means algorithm [5] to divide them into a number of coherent groups based on their spatial distributions. The algorithm ensures that the keypoints in a group are always in a neighboring region. Figure 6 shows the clustering result of the official eBay webpage (left-hand side) and a phishing eBay page (right-hand side), where k=4 groups are circled using different colors. Based on the results, we match groups of keypoints between the two webpages by voting; that is, for a group of keypoints, A, on the authentic page, a group of keypoints, B, on the suspect page will be considered as A's mapping if most of the keypoints in A match keypoints in B. We then define a keypoint as geographically matched if its group is a mapping of its corresponding keypoint's group. In cases where two pages are dissimilar, the number of matched points will be small so that the clustering cannot even be performed. For example, Fig. 7 shows the matching result of pages from different sites. Although a few of match, none of them are considered geographically matched as no clusters are found. Given the geographical matching information, we define the similarity degree between two webpages by the ratio of geographically matched keypoints to all the identified keypoints on the two pages. As phishing pages are similar to the authentic pages they try to mimic, we use the similarity degree between a suspect webpage and the authentic page to determine whether the suspect is indeed a counterfeit, which is normally designed to steal users' sensitive information.

4  Performance Evaluation

Table 1: The Top 5 Phishing Target Sites
Sites Number of Records CR FNR FPR
eBay 70196.8% 0.0% 0.1%
PayPal 63297.7% 0.0% 0.1%
Marshall & Ilsley Bank 13897.7% 0.0% 0.1%
Charter One Bank 11698.0% 0.0% 0.1%
Bank of America 5195.4% 2.0% 2.1%
Total Number of Phishing Target Pages: 300 pages in 74 sites.
CR: Correct Rate; FNR: False Negative Rate; FPR: False Positive Rate
According to a survey conducted by Secure Computing [1], more than half the phishing attacks in 2007 were targeted famous websites, such as eBay, a popular online auction service, and PayPal, a popular online billing service. For this reason, we collected a number of real-life phishing webpages that targeted the top 5 phishing targets, namely eBay, PayPal, Marshall and Ilsley Bank, Charter One Bank, and Bank of America. In addition, we collected 300 webpages of well-known online bank and auction services, which are often targeted in phishing attacks in order to observe the distribution of 1) the similarity degree between a phishing page and its corresponding authentic page, and 2) the similarity between two unrelated webpages. We find that the former is normally a small value around zero, while the latter is normally a large value around one. Based on our observations, we empirically set the threshold to 0.6 and determine that a suspect page is a phishing page if its similarity degree is higher than this threshold. The evaluation results listed in Table 1 show that our scheme achieves a high degree of accuracy that ranges between 95% and 98%; moreover, the error rates, i.e., the false positive rate and false negative rate, are much lower than 1% in most cases.

Case Studies

boa_match.png
Figure 8: Case study: the login page and a phishing page of Bank of America
In the following, we explain how our detection scheme works in real-life cases. Although phishers endeavor to make phishing pages indistinguishable from the authentic pages to deceive users, they usually make some modifications to evade phishing detection techniques. In our first case, which is a typical example, the phishers add an advertisement banner to the phishing page to slightly alter the layout. The change may not be noticed by unwary users, but it may make anti-phishing tools less effective. Figure 8 shows the authentic Bank of America login page on the left-hand side, and a phishing page with an advertisement banner inserted on the right-hand side. Because the change is minor and Internet users are accustomed to advertisements on webpages, the inserted banner may go unnoticed by users. Even so, the banner changes the aspect ratio of the page and adds a great deal of red to the image, which will reduce the detection ability of anti-phishing solutions based on color distributions and page layout. In contrast, the effectiveness of our scheme is not degraded because it is based on local discriminative keypoints, which are invariant to changes in image layout and color distribution, the banner insertion does not affect the effectiveness of our scheme. It is worth noting that such banners not only help phishers evade anti-phishing solutions, but also make money for the phishers every time a banner is displayed on a user's computer.
Our second case demonstrates another common phishing strategy whereby phishers alter the input form by adding or removing fields. For example, in the Bank of American case shown in Fig. 8, the phishers added an additional "Enter Passcode" field to the phishing page. As a result, unwitting users may provide sensitive information without realizing that such information is not requested on the authentic page. In other cases, phishers add fields that ask for more private data from users, such as credit card numbers and social security numbers. It is difficult for most users to detect that these modifications are fake because people do not usually remember exactly what fields should appear on an input form. Once again, this case demonstrates the efficacy of our scheme. Even though both the advertisement banner and the additional field alter the page layout and aspect ratio, our CCH descriptor still yields a near perfect matching between the keypoints of the phishing and authentic pages.
The above examples demonstrate how phishers can alter the design of an authentic webpage to deceive unwary users. Nevertheless, to ensure that phishing pages are similar to the authentic pages, most of the main elements of the original page must to be preserved. Our scheme is capable of detecting the similarity between fake pages and the original pages regardless of the types of changes.

5  Conclusion

Phishing has become a major threat to information security and personal privacy, and many people have been cheated out of vast sums of money as a consequence. As phishing pages often look almost identical to their target pages, many anti-phishing solutions, such as content analysis and HTML code analysis, rely on this property to detect fake webpages. However, phishers are now countering these detection techniques by composing phishing pages with non-analyzable elements, such as images and Flash objects, even though the pages still look like the authentic pages. To address this problem, we propose an image-based phishing detection scheme that uses the Contrast Context Histogram, a descriptor for describing local-invariant discriminative keypoints. The results of evaluations and case studies show that our scheme can detect phishing pages with a high degree of accuracy and only a few false alarms. Moreover, as our scheme is purely based on passive monitoring of web pages that users browse, it is orthogonal to other solutions and therefore can be freely integrated with existing prevention and detection schemes to fight phishing together.

Acknowledgement

This work was supported in part by Taiwan Information Security Center (TWISC), National Science Council under the grants NSC97-2219-E-001-001 and NSC97-2219-E-011-006. It was also supported in part by Taiwan E-learning and Digital Archives Programs (TELDAP) sponsored by the National Science Council of Taiwan under the grants NSC98-2631-001-011 and NSC98-2631-001-013.

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Sheng-Wei Chen (also known as Kuan-Ta Chen)
http://www.iis.sinica.edu.tw/~swc 
Last Update September 19, 2017