用户确实对欺骗有反应

M. L. Rahman, Daniel Timko, H. Wali, Ajaya Neupane
{"title":"用户确实对欺骗有反应","authors":"M. L. Rahman, Daniel Timko, H. Wali, Ajaya Neupane","doi":"10.1145/3577923.3583640","DOIUrl":null,"url":null,"abstract":"Text phish messages, referred to as Smishing (SMS + phishing) is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Drawing inspiration by the works of Tu et al. on Robocalls and Tischer et al. on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline. Based on our results, we pinpoint essentially message attributes and demographic features that contribute to a statistically significant change in the response rates to smishing attacks.","PeriodicalId":387479,"journal":{"name":"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Users Really Do Respond To Smishing\",\"authors\":\"M. L. Rahman, Daniel Timko, H. Wali, Ajaya Neupane\",\"doi\":\"10.1145/3577923.3583640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Text phish messages, referred to as Smishing (SMS + phishing) is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Drawing inspiration by the works of Tu et al. on Robocalls and Tischer et al. on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline. Based on our results, we pinpoint essentially message attributes and demographic features that contribute to a statistically significant change in the response rates to smishing attacks.\",\"PeriodicalId\":387479,\"journal\":{\"name\":\"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3577923.3583640\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3577923.3583640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

文本网络钓鱼消息,称为Smishing (SMS + phishing),是一种社会工程攻击,通过创建虚假文本消息来引诱用户响应这些消息。这些信息的目的是获取用户凭证,在手机上安装恶意软件,或者发起诈骗攻击。他们要求用户回复他们的信息,点击将他们重定向到网络钓鱼网站的URL,或者拨打所提供的号码。从Tu等人在Robocalls和Tischer等人在USB驱动器上的作品中获得灵感,本文研究了为什么欺骗有效。因此,我们设计了诈骗实验,并向265个用户发送了钓鱼短信,以衡量诈骗攻击的有效性。我们向参与者发送了八条假短信,并记录了他们的点击、回复和呼叫反应,以及他们在测试后调查中的反馈。我们的结果显示,16.92%的参与者可能会被我们的欺骗攻击所欺骗。为了测试重复网络钓鱼,我们让一组随机选择的参与者接受第二轮的钓鱼攻击,其中的消息与他们在第一轮收到的消息不同。结果,我们观察到有12.82%的用户可能再次遭到攻击。使用逻辑回归,我们观察到,与CLICK相比,用户REPLY和CLICK操作的组合增加了用户响应我们的欺骗消息的几率。此外,当将Facebook和沃尔玛实体场景与IRS基线进行比较时,我们发现了类似的统计学显著增长。根据我们的结果,我们确定了消息属性和人口统计特征,这些特征导致了对欺骗攻击的响应率在统计上的显著变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Users Really Do Respond To Smishing
Text phish messages, referred to as Smishing (SMS + phishing) is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Drawing inspiration by the works of Tu et al. on Robocalls and Tischer et al. on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline. Based on our results, we pinpoint essentially message attributes and demographic features that contribute to a statistically significant change in the response rates to smishing attacks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Tackling Credential Abuse Together Comparative Privacy Analysis of Mobile Browsers Confidential Execution of Deep Learning Inference at the Untrusted Edge with ARM TrustZone Local Methods for Privacy Protection and Impact on Fairness Role Models: Role-based Debloating for Web Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1