ࡱ> tyM %bjbj== \WW|kl$$$$$,,,PV,\,dEh"-.P0P0P00M8j CT G$ H7^M8HHK$$P00eKKKH*$P0:0KHKRKN&z>p0- ,I^dpT0ET_?Jl_pK$$$$ Phishing for Answers: Factors Influencing A Participant's Ability to Categorize Email Tim Martin, PhD Capella University March 25, 2009 Abstract Identity theft is a widespread crime against consumers and phishing is a popular method used to steal a person's identity or financial information. Anyone with an email account is a potential victim and organizations need to stress to their employees the risks of email. To address this problem, this research study sought to determine if consumer education was a solution to combat identity theft. Specific characteristics including age, gender, education, knowledge of phishing or online habits were analyzed to determine their impact on the participants ability to identify legitimate or fraudulent email messages. Quantitative data was collected by showing participants ten email messages and quizzing them on their ability to correctly categorize the messages. The results show the variables listed above did influence the participants ability to correctly identify email messages. TABLE OF CONTENTS  TOC \o "1-2" \h \z  HYPERLINK \l "_Toc225686346" Abstract  PAGEREF _Toc225686346 \h 2  HYPERLINK \l "_Toc225686347" I. INTRODUCTION  PAGEREF _Toc225686347 \h 4  HYPERLINK \l "_Toc225686348" A. Background  PAGEREF _Toc225686348 \h 4  HYPERLINK \l "_Toc225686349" B. Research Questions  PAGEREF _Toc225686349 \h 5  HYPERLINK \l "_Toc225686350" C. Application  PAGEREF _Toc225686350 \h 5  HYPERLINK \l "_Toc225686351" D. Assumptions and Limitations  PAGEREF _Toc225686351 \h 6  HYPERLINK \l "_Toc225686352" E. Nature of the Study  PAGEREF _Toc225686352 \h 6  HYPERLINK \l "_Toc225686353" II. METHODOLOGY  PAGEREF _Toc225686353 \h 8  HYPERLINK \l "_Toc225686354" A. Philosophy  PAGEREF _Toc225686354 \h 8  HYPERLINK \l "_Toc225686355" B. Methodology  PAGEREF _Toc225686355 \h 8  HYPERLINK \l "_Toc225686356" C. Population  PAGEREF _Toc225686356 \h 9  HYPERLINK \l "_Toc225686357" D. Instrumentation and Data Collection  PAGEREF _Toc225686357 \h 9  HYPERLINK \l "_Toc225686358" III. RESULTS  PAGEREF _Toc225686358 \h 10  HYPERLINK \l "_Toc225686359" A. Findings  PAGEREF _Toc225686359 \h 10  HYPERLINK \l "_Toc225686360" B. Summary  PAGEREF _Toc225686360 \h 10  HYPERLINK \l "_Toc225686361" IV. DISCUSSION  PAGEREF _Toc225686361 \h 12  HYPERLINK \l "_Toc225686362" A. Analysis  PAGEREF _Toc225686362 \h 12  HYPERLINK \l "_Toc225686363" B. Interpretation  PAGEREF _Toc225686363 \h 13  HYPERLINK \l "_Toc225686364" C. Recommendations  PAGEREF _Toc225686364 \h 15  HYPERLINK \l "_Toc225686365" D. Summary  PAGEREF _Toc225686365 \h 15  HYPERLINK \l "_Toc225686366" V. REFERENCES  PAGEREF _Toc225686366 \h 17  HYPERLINK \l "_Toc225686367" VI. SURVEY INSTRUMENT  PAGEREF _Toc225686367 \h 22  I. INTRODUCTION A. Background Because of the growing trend to bank online, phishing will remain one of the most visible threats in 2006 (Business Wire, 2006). With more people participating in online activities like reading email or shopping, the likelihood of becoming a victim of identity theft increases. A discussion by Krause (2005) suggests the possibility of education as a deterrent to phishing and Deborah Majoras, Federal Trade Commission Chair, agrees (FTC & Partners, 2005). Consumer education and training ranges from ensuring the public's awareness of phishing and identify theft to prosecuting those guilty of identity theft and making the trial and conviction process public knowledge. A 2006 article, however, has shown the level of detail of a phishing technique is so high (Krebs, 2006) that even a very knowledgeable consumer may become a victim. In the Krebs article, members of a credit union in Utah became victims because the phishers used very detailed information to appear legitimate. The phishers sent emails looking like they came from the credit union, had links with Secure Sockets Layer (SSL) certificates and even included the first six digits of member's account number. Most members were unaware that all accounts started with the same first six digits so they did not question the authenticity of the phishing attempt. Joan Lockart, Vice President of Marketing at GeoTrust, a company entrusted with ensuring SSL certificates are issued to only valid businesses, said certificate issuing employees did not question the authenticity of this request because it appeared to be from a legitimate business (Krebs, 2006, p. 3). Because this site was issued a valid certificate, education and training may not have prevented consumers from accessing it and getting phished. Additionally, phishers are not just targeting consumers. They are going after large corporations in an attempt to gather proprietary information and to use it as a form of web extortion. They use email appearing to be from a headquarters or central office within the organization and ask employees to provide updates on their projects, financial information or research and development information not currently released to the general public. Because this is a simple process and the emails look authentic, employees respond and later realize the information they provided has been sold to a competitor or is being used against their company for financial gain (Anonymous, 2005). B. Research Questions A previous study focused on why phishing works (Dhamija, Hearst & Tygar, 2006) and explored social engineering as a factor in phishing attacks. Another study (Leyden, 2006) focused on web links and how even sophisticated users were fooled. Little research, however, was available on the variables influencing the learning process of consumers specific to phishing so this lack of documentation led to the following research questions: 1. How do the characteristics of age and gender impact a participant's ability to correctly identify legitimate and fraudulent emails? 2. What other characteristics impact a participant's ability to correctly identify legitimate and fraudulent emails? C. Application This study has broad applications because anyone who has an email address is a potential phishing target and many organizations educate their employees on web security but do not measure the effectiveness of their training program. For this study, the factors of age, gender, education (defined as college credits), knowledge of phishing and online shopping habits were explored to determine if they influenced a participant's ability correctly categorize emails as legitimate or fraudulent. The results may be applied to corporate training programs on topics beyond identity theft and phishing as a way to determine the success of such a program. D. Assumptions and Limitations The first assumption was that participants would be willing to provide their generic demographic information to the researcher and some participants did not, so their results were not included. A second assumption was that no technological advance would eliminate the need for identity theft and phishing education because as the technology gets better at identifying fraudulent email and blocking it, phishers get better at hiding their intentions and finding ways around the improved technology. The third assumption was that participants receiving the training would remember the information or refer to the handouts that were provided for future use. Because participants may not currently be receiving phishing emails, they may not remember what was covered in the training course. The first limitation was that correctly identifying emails will not completely eliminate the possibility of someone becoming a victim of identity theft - this research was limited to email and did not cover advanced phishing techniques aimed at cell phone users, voice-over-IP (vishing) or SMS users (smishing). Additionally, the survey participants were limited to employed adults, knowing anyone with an email account (child or adult, and employed or not) are potential victims. E. Nature of the Study This study consisted of 153 adult employed participants with an email account. The participants completed a consent form and brief demographic survey prior taking a quiz testing their ability to appropriately identify emails. Following the quiz, participants were given information on identity theft and phishing, to include specific indicators of phishing emails and then they retook the same quiz. After the second quiz, participants were shown the results of their quiz, given information on why a particular email was legitimate or fraudulent and provided sources on where to get more information on identity theft and phishing. Finally, the results of the first and second quizzes were compared to determine if age, gender, education, knowledge of phishing or online habits had an impact on the difference in test scores. II. METHODOLOGY A. Philosophy Numerous articles and studies on phishing and identity theft have been written warning of the dangers associated with replying to such requests, yet phishing is still a problem because consumers are willing to provide their personal information to individuals and organizations that request it. This research focused on a viewpoint of consumer education and training, how it related to consumer responses and if age, gender, education, knowledge of phishing or online habits influenced a participant's ability to retain the information following a training program. As consumers gain more knowledge about phishing and identity theft, the phishers get more advanced, making it harder to detect fraudulent email. B. Methodology This study was a fixed design involving a quasi-experimental quantitative study with pre- and post-tests administered to the same group of participants. Participants completed a consent form prior to the survey, giving them the choice of opting out of the study at any time without any repercussions. Then, generic demographic information was collected. This information was used to track the differences in test scores. Next, participants were given a quiz to test their knowledge of phishing. This quiz and demographic survey were similar to the one used at the University of South Carolina, Beaufort, where the success of phishing was studied (Boulware, Folsom & Guillory, 2005). This study, however, went beyond the previous study in that the participants were tested twice. After the initial test, the results were collected and participants were given information on identity theft and phishing. Finally, participants were given the same test again and the results from both tests were compared to determine if age, gender, education, knowledge of phishing or online habits influenced a change in their test scores. C. Population The exact number of adults employed in Utah with an email account is not known, but is expected to be over one hundred thousand based on census statistics (U. S. Census, 2009) and computer ownership (Ohlemacher, 2005). Therefore, a sample size of over 383 was preferred. Due to time and geographic constraints, only 153 participants were contacted. The population consisted of participants from various organizations including real estate employees, state employees, federal employees including those in medical, financial, human resources, information technology and security positions, financial advisors, financial institutions and physicians. All participants were informed of the format of the test, given the option of not participating, made aware of how their identity was protected (no personal information was collected) and told how the results would be published. D. Instrumentation and Data Collection The instrumentation was similar to one used by Boulware, Folsom and Guillory (2005) but with a few modifications. Their survey was sufficient for testing the ability to recognize phishing and legitimate emails, but no information on phishing was provided and only a single test was conducted. For this research, the survey consisted of a two-page survey (shown in the Appendix). Page one consisted of general demographic information used to establish a baseline for participants. Page two, which was used twice, was the answer sheet to the quiz used to track the participant's ability to correctly identify emails. Scores from each quiz were based on the number of correct choices the participants made when shown email messages. Because each quiz consisted of 10 questions, the maximum score a participant could receive was 10 out of 10. After both quizzes, the pre- and post- results were compared. The outcome of the tests provided additional information for the research questions. III. RESULTS A. Findings Of the 153 received surveys, 141 were usable, meaning all answers were completed, there were no multiple answers and all answers were legible. No participant answered all the quiz questions correct on the pre-test and two answered all the quiz questions correct on the post-test. A breakdown of the participant's demographic information follows: Age categories included 18-35 (38), 36-50 (46) and 51 and above (57). Gender included 56 female and 85 male participants. Education level included no college (5), some college (27), Bachelors degree (69), Masters degree (33) and beyond Masters (7). Knowledge of phishing included none (21), somewhat (99), knowledgeable (19) and expert (2). Online connectivity was either 1-5 times per week (27) or 6 or more times per week (114). Paying bills online resulted in yes (90) and no (51). Checking account balances resulted in yes (119) and no (22). B. Summary The data collected showed no single variable as having a significant impact on pre and post-test scores. Specific to the research questions, the data showed age and gender impacted the participants ability to correctly identify email messages. Males scored higher on the pre-test (6.55) than did females (6.50), but females increased their scores more (6.91) than males (6.82). Additionally, factors of education, knowledge of phishing, frequency on online connectivity, paying bills online and checking balances online impact a participants ability to identify email messages, although not significantly. IV. DISCUSSION A. Analysis Age and gender were explored to determine their impact on a participants success in correctly identifying email messages for research question one. The data in Table 1 shows age impacted a participants scores. Each age category scored differently on the pre and post-tests. The 18-35 category and 51 or above category increased their scores the most (6.87 to 7.26 and 6.23 to 6.63) and the 36-50 category increasing the least (6.63 to 6.80). Gender also impacted test scores as shown in Table 1. Males scored higher on the pre-test (6.55) than females (6.50) and females scored higher on the post-test (6.91) than males (6.82). Table 1. Age and Pre/Post-Test Scores  Table 2. Gender and Pre/Post-Test Scores Exploring the second research question showed education did impact the participants test scores, as shown in Table 3. The some college category scored the lowest on the pre-test (6.37) with beyond Masters scoring the highest (7.29). On the post-test, the Masters category scored the highest (6.55) and no college scored the lowest (6.60). Some college had the largest increase in test scores from 6.37 to 6.81 and beyond Masters had their test scores decrease between the pre- (7.29) and post-tests (6.86). The two participants answering all the post-test questions correctly were in the some college and Masters categories. Table 3. Education and Pre/Post-Test Scores B. Interpretation The data was interpreted by realizing all the factors influencing the participants test scores. It was expected that age, gender and education would impact test scores. Knowledge of phishing, frequency of online connectivity, paying bills online and checking balances online were expected to have a larger impact on test scores because these showed the participants confidence level with online connectivity. In other words, a person with little or not confidence in online transactions would not be expected to conduct business (pay bills or check balances) online and would be not be expected to recognize phishing emails. People who frequently engaged in online activities were expected to score higher as their confidence and awareness would be higher. Knowledge of phishing, frequency of connectivity and paying bills online did not have a significant impact on post-test scores. Age was inconsistent in that the middle age group increased their scores the most. As age increased, the pre and post-test scores decreased, meaning each age group improved their scores from the first to the second test, but the score for each age group was lower than the previous group. Additionally, as age increased, the increase of scores from the pre- and post-tests decreased, possibly indicating the participants did not learn as much or retain as much from the presentation. Males had a higher pre-test score than females and both genders increased their post-test scores, but females increased more. However, the difference between the genders on pre and post-test scores is .33 of a point, meaning their overall increase was very small. Additionally, two participants (one male and one female) answered all the questions correctly. All education categories except for beyond Master's increased test scores between pre and post-tests with participants in the some college category increasing by .44 of a point. Surprisingly, participants in the beyond Master's category decreased their scores between the pre (7.29) and post-test (6.86), meaning education did not impact their scores. Also of note is that each category scored higher than the previous one on pre and post-tests except for no college (6.40) and some (6.37) (pre-test) and beyond Master's on the post-test, as shown in table 3. C. Recommendations Future studies need to focus on the impact of correctly identifying a message as phishing or legitimate, rather than just counting the number of correct answers. For example, in this study, four of the email messages were legitimate and the other six were phishing. Participants may have correctly identified all the legitimate messages but may have marked a phishing email as legitimate. Doing so does not assume they will respond to it if they mark it as legitimate but does increase the chance they might. Conversely, marking a legitimate email as a phish is a wrong answer on the quiz but is safer because this may prevent consumers from responding and providing personal information to any email messages. Additionally, future studies not only should focus on the factors that influence the participants ability, but also on why. For example, this study shows age as having an impact on the participants ability to categorize email messages correctly (the 18-35 group had the highest pre and post-test scores and these scores decreased with age) but does not indicate why. Older participants may be more likely to retain information from a training program because they connect online less frequently and are more interested in knowing the risks associated with online activity, resulting in their increased test scores. Because the success rate of a phish is between 5% (Knight, 2004; Thompson, 2006) and 11% (Jakobsson & Ratkiewicz, 2006), knowing why factors influence a participant's ability to recognize phishing emails is important to keep consumers protected from identity theft and to develop appropriate methods to educate and train employees and consumers. D. Summary Identity theft will continue to be a crime against consumers and phishing will continue to be a common method used to steal identities and protected information. Advances in software will prevent some types of phishing, as will anti-phishing tool bars, black and white-lists for domain names, active participation from ISPs tracking the creation of fraudulent web pages and stiffer penalties for criminals who commit these crimes. However, educating people to make them knowledgeable consumers, will continue to be an area in need of improvement because phishing is based on social engineering. As the technologies get better at finding phishers, phishers will get more creative and will find ways around detection devices, software tools and other preventative measures. This study explored demographic variables to determine how they influence participants' scores. Each age group scored differently and gender influenced test scores as did education level. However, these influences were not consistent. It was expected that more educated participants would perform better on the tests and increase their scores the most but this was not found in all education categories, therefore, leading to the need for more research. As previously stated, anyone who has an email address is a potential phishing target. Many organizations educate their employees on web security but do not measure the effectiveness of their training programs. 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The state of security on the Internet. Http://portal.acm.org/citation. cfm?id=1016961.1016971 Ratcliffe, M. (2005). Card cos. combat rising tide of e-mail phishing. Http://www.allbusiness. com/transportation-communications/transportation-services/4157615-1.html Warren, S. (2005). 12 steps to avoid phishing scams. Tech Republic. Http://downloads. techrepublic.com.com/abstract.aspx?&q=avoid+phishing& docid=172840 Williams, P. (2006). E-filing? Beware of 'phishing' scams. Http://www.msnbc.msn.com/id /12171437/ U.S. Census Bureau. (2009). Http://quickfacts.census.gov/qfd/states/49000.html Zumpft, C. (2006). PhishGuard delivers free Firefox anti-phishing service. Http://www.prweb. com/releases/2006/5/prweb379628.htm VI. SURVEY INSTRUMENT Instructions Please circle the appropriate answer and note that only one answer per question is permitted. 1. Age 18-35 36-50 51 or above 2. Gender Female Male 3. Education Level No college Some college Bachelor's Degree Master's Degree Beyond Master's 4. Knowledge of phishing None (didn't know what it was) Somewhat (have heard of it) Knowledgeable (know what a phishing email looks like) Expert (have read articles and research on phishing) 5. How many times in the past week have you been online? 1-5 times 6 times or more 6. Do you pay bills online? Yes No 7. Do you check your account balances (bank or credit union, credit card, loan, online? Yes No For the questions listed below, circle the correct answer for the email you will be shown: 1. CFCU Legitimate Phishing 2. Chase Legitimate Phishing 3. Chase Legitimate Phishing 4. SBC Legitimate Phishing 5. eBay Legitimate Phishing 6. Comerica Legitimate Phishing 7. Capital One Legitimate Phishing 8. PayPal Legitimate Phishing 9. PayPal Legitimate Phishing 10. Mastercard Legitimate Phishing Note: From "Gone Phishing," by Boulware, R. D., Folsom, W. D., & Guillory, M. D., 2005, Business & Economic Review, 52(1), 29-31. Copyright 2005 by the Name of Copyright Holder. Adapted with permission.      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