网络钓鱼识别研究综述

付溪; 李晖; 赵兴文

doi:10.11959/j.issn.2096-109x.2020062

您当前的位置：

首页 >

文章列表页 >

网络钓鱼识别研究综述

综述 | 更新时间：2024-06-03

- 网络钓鱼识别研究综述
- Survey on phishing detection research
- 网络与信息安全学报 2020年6卷第5期页码：1-10
- 作者机构：
- 作者简介：
  
  [ "付溪（1996- ），女，陕西西安人，西安电子科技大学硕士生，主要研究方向为信息安全" ]
  [ "李晖（1968- ），男，河南灵宝人，博士，西安电子科技大学教授、博士生导师，主要研究方向为密码学、无线网络安全、云计算安全、信息论与编码理论" ]
  [ "赵兴文（1977- ），男，广西玉林人，博士，西安电子科技大学副教授，主要研究方向为人工智能在网络安全中的应用、多方参与的数据安全共享、匿名认证等保护隐私的密码协议" ]
- 基金信息：
  
  国家重点研发计划;The National Key R＆D Program of China(2016YFB0801001);移动互联网陕西省创新团队项目;Mobile Internet Shaanxi Innovation Team Project(2018TD-007);西安市科技计划项目;Xi'an Science and Technology Plan Project(201809168CX9JC10)
- DOI：10.11959/j.issn.2096-109x.2020062
  中图分类号： TN915
- 网络首发：2020-10，
  
  纸质出版：2020-10-15
- 稿件说明：
移动端阅览
付溪, 李晖, 赵兴文. 网络钓鱼识别研究综述[J]. 网络与信息安全学报, 2020,6(5):1-10.

Xi FU, Hui LI, Xingwen ZHAO. Survey on phishing detection research[J]. Chinese Journal of Network and Information Security, 2020, 6(5): 1-10.
付溪, 李晖, 赵兴文. 网络钓鱼识别研究综述[J]. 网络与信息安全学报, 2020,6(5):1-10. DOI： 10.11959/j.issn.2096-109x.2020062.

Xi FU, Hui LI, Xingwen ZHAO. Survey on phishing detection research[J]. Chinese Journal of Network and Information Security, 2020, 6(5): 1-10. DOI： 10.11959/j.issn.2096-109x.2020062.

摘要

随着互联网的不断发展，网络钓鱼给人们日常生活带来的威胁与日俱增。网络钓鱼识别技术是对抗钓鱼攻击的核心安全技术，可以帮助人们有效避免钓鱼攻击引起的安全威胁。首先，从网络钓鱼的基本概念入手，详细分析了网络钓鱼识别技术的研究现状，然后，对目前网络钓鱼识别的应用场景进行了归纳和总结，最后，对今后可能的研究方向进行了讨论。

Abstract

With the continuous development of the internet

the threat posed by phishing to people's daily lives is increasing.As a core security technology against phishing attacks

phishing detection technology can help people effectively avoid security threats caused by phishing attacks.Firstly

starting with the basic concepts of phishing

the current application scenarios of phishing identification were summarized.Then

the research status of phishing identification technology in detail were analyzed.Finally

possible future research directions were discussed.

关键词

Keywords

references

BLEAU H . 2017 Global fraud and cybercrime forecast [EB ] .

Phishing activity trends report 3rd quarter 2019 [R ] .

王惟 . 反钓鱼技术综述 [J ] . 山东广播电视大学学报 , 2011 ( 3 ): 45 - 46 ,49.

WANG W . Review of anti-phishing technology [J ] . Journal of Shandong Radio and TV University , 2011 ( 3 ): 45 - 46 ,49.

李江丰 , 王玮 . 钓鱼网站的识别与分析方法研究 [J ] . 通信管理与技术 , 2018 ( 3 ): 62 - 64 .

LI J F , WANG W . Research on recognition and analysis methods of phishing websites [J ] . Communications Management and Technol-ogy , 2018 ( 3 ): 62 - 64

沙泓州 , 刘庆云 , 柳厅文 , 等 . 恶意网页识别研究综述 [J ] . 计算机学报 , 2016 ( 3 ): 529 - 542 .

SHA H Z , LIU Q Y , LIU T W.et al . Review of malicious web rec-ognition [J ] . Journal of Computers , 2016 ( 3 ): 529 - 542 .

Google safe browsing APIs [EB ] .

HAN W , CAO Y , BERTINO E , et al . Using automated individual white-list to protect web digital identities [J ] . Expert Systems with Applications , 2012 , 39 ( 15 ).

PRAKASH P , KUMAR M , KOMPELLA R R , et al . PhishNet:predictive blacklisting to detect phishing attacks [C ] // 29th INFOCOM . 2010

ZHANG J , PORRAS P A , ULLRICH J . Highly predictive blacklisting [C ] // 17th USENIX Security Symposium 2008 .

SHENG S , WARDMAN B , WARNER G , et al . An empirical analysis of phishing blacklists [C ] // The 6th Conf.Email Anti-Spam (CEAS)

GASTELLIER-PREVOST S , GRANADILLO G G , LAURENT M . Decisive heuristics to differentiate legitimate from phishing sites [C ] // Network ＆ Information Systems Security . 2011 .

KANG L C , CHANG E H , SZE S N , et al . Utilisation of website logo for phishing detection [J ] . Computers ＆ Security , 2015 ( 54 ): 16 - 26 .

DUNLOP M , GROAT S , SHELLY D . GoldPhish:using images for content-based phishing analysis [C ] // Fifth International Conference on Internet Monitoring ＆ Protection . 2010 .

HUH J H , KIM H . Phishing detection with popular search engines:simple and effective [M ] // Foundations and Practice of Security . Berlin Heidelberg:Springer , 2011 .

JAIN A K , GUPTA B B . Two-level authentication approach to protect from phishing attacks in real time [J ] . Ambient Intelligence and Humanized Computing , 2018 , 9 ( 6 ): 1783 - 1796 .

TAN C L , KANG L C , WONG K S , et al . PhishWHO:phishing webpage detection via identity keywords extraction and target domain name finder [J ] . Decision Support Systems , 2016 ( 88 ): 18 - 27 .

VARSHNEY G , MISRA M , ATREY P K . Improving the accuracy of search engine based anti-phishing solutions using lightweight features [C ] // 11th ICITST 2016 .

ZHANG Y , HONG J I , CRANOR L F . Cantina:a content-based approach to detecting phishing web sites [C ] // 16th WWW , 2007 .

RAO R S , PAIS A R . Jail-phish:an improved search engine based phishing detection system [J ] . Computers ＆ Security , 2019 ( 83 ): 246 - 267 .

LE A , MARKOPOULOU A , FALOUTSOS M . PhishDef.URL names say it all [J ] . CoRR abs/1009.2275 , 2010

MA J , SAUL L K , SAVAGE S , et al . Beyond blacklists:learning to detect malicious Web sites from suspicious URLs [C ] // KDD . 2009 .

YUAN H , CHEN X , LI Y , et al . Detecting phishing websites and targets based on URLs and webpage links [C ] // 24th ICPR 2018 .

GARERA S , PROVOS N , CHEW M , et al . A framework for detection and measurement of phishing attacks [C ] // ACM Workshop on Recurring Malcode . 2007 .

MC-GRATH D K , GUPTA M . Behind phishing:an examination of phisher modi operandi [C ] // 5th NSDI 2008 .

LI Y , YANG Z , XU C , et al . A stacking model using URL and HTML features for phishing webpage detection [J ] . Future Generation Comp Syst , 2019 ( 94 ): 27 - 39 .

SAHINGOZ O K , BUBER E , DEMIR O , et al . Machine learning based phishing detection from URLs [J ] . Expert Syst Appl , 2019 ( 117 ): 345 - 357 .

YANG W , ZUO W , CUI B . Detecting malicious URLs via a keyword-based convolutional gated-recurrent-unit neural network [J ] . IEEE Access , 2019 ( 7 ): 29891 - 29900 .

YANG P , ZHAO G , ZENG P . Phishing website detection based on multidimensional features driven by deep learning [J ] . IEEE Access , 2019 ( 7 ): 15196 - 15209 .

MARCHAL S , ARMANO G , GRONDAHL T , et al . Off-the-hook:an efficient and usable client-side phishing prevention application [J ] . IEEE Trans.Computers , 2017 , 66 ( 10 ): 1717 - 1733 .

RAO R S , PAIS A R . Detection of phishing websites using an efficient feature-based machine learning framework [J ] . Neural Computing and Applications , 2019 , 31 ( 8 ): 3851 - 3873 .

SHIRAZI H , BEZAWADA B , RAY I.Unbiased phishing detection using domain name based features . [C ] // SACMAT . 2018 .

MARCHAL S , SAARI K , SINGH N , et al . Know your phish:novel techniques for detecting phishing sites and their targets [C ] // 36th ICDCS . 2016 .

RAMESH G , KRISHNAMURTHI I , KUMAR K S S . An efficacious method for detecting phishing webpages through target domain identification [J ] . Decision Support Systems , 2014 , 61 : 12 - 22 .

XIANG G , HONG J I , ROSÉ C P , et al . CANTINA+:a feature-rich machine learning framework for detecting phishing web sites [J ] . ACM Trans Inf Syst Secur , 2011 , 14 ( 2 ): 21:1 - 21:28 .

FATT J C S , LENG C K , NAH S S . Phishdentity:leverage website favicon to offset polymorphic phishing website [C ] // ARES . 2014 .

CHIEW K L , CHOO J S F , SZE S N , et al . Leverage website favicon to detect phishing websites [J ] . Security and Communication Networks , 2018 :11.

周诚诚 , 张代远 . 利用图像识别技术过滤海量可疑钓鱼网站 [J ] . 计算机技术与发展 , 2012 ( 11 ): 246 - 249 .

ZHOU C C , ZHANG D Y . Using image recognition technology to filter mass suspicious phishing sites [J ] . Computer Technology and Development , 2012 ( 11 ): 246 - 249 .

肖洪云 . 图像识别在钓鱼检测中的应用 [J ] . 沧州师范学院学报 , 2012 ( 3 ): 75 - 79 .

XIAO H Y . The application of image recognition to the fishing de-tection [J ] . Journal of Cangzhou Normal University , 2012 ( 3 ): 75 - 79 .

HARA , YAMADA , MIYAKE . Visual similarity-based phishing detection without victim site information [C ] // ICICS . 2009 .

FANG Y , HUANG C , LIU L , et al . Research on malicious JavaScript detection technology based on LSTM [J ] . IEEE Access , 2018 , 6 : 59118 - 59125 .

DONG Z , KAPADIA A , BLYTHE J , et al . Beyond the lock icon:real-time detection of phishing websites using public key certificates [C ] // eCrime . 2015 .

MENSAH P , BLANC G , OKADA K , et al . AJNA:anti-phishing JS-based visual analysis,to mitigate users' excessive trust in SSL/TLS [C ] // 4th BADGERS@RAID . 2015 .

DRURY V , MEYER U . Certified phishing:taking a look at public key certificates of phishing websites [C ] // 15th SOUPS @ USENIX Security Symposium . 2019 .

胡向东 , 刘可 , 张峰 , 等 . 基于页面敏感特征的金融类钓鱼网页检测方法 [J ] . 网络与信息安全学报 , 2017 ( 2 ): 35 - 42 .

HU X D , LIU K , ZHANG F , et al . Financial phishing detection method based on sensitive characteristics of webpage [J ] . Chinese Journal of Network and Information Security , 2017 ( 2 ): 35 - 42 .

方勇 , 龙啸 , 黄诚 , 等 . 基于LSTM与随机森林混合构架的钓鱼网站识别研究 [J ] . 四川大学学报（工程科学版） , 2018 ( 5 ).

FANG Y , LONG X , HUANG C , et al . Research on classifying phishing URLS using hybrid architecture of LSTM and random forest [J ] . Advanced Engineering Sciences , 2018 ( 5 ).

CHIEW K L , TAN C L , WONG K , et al . A new hybrid ensemble feature selection framework for machine learning-based phishing detection system [J ] . Inf Sci , 2019 , 484 : 153 - 166 .

BABAGOLI M , AGHABABA M P , SOLOUK V . Heuristic nonlinear regression strategy for detecting phishing websites [J ] . Soft Comput , 2019 , 23 ( 12 ): 4315 - 4327 .

Phishing activity trends report 4th quarter 2016 [EB ] .

Phishing activity trends report 1st-3rd quarter 2015 [EB ] .

Microsoft security intelligence report [R ] . 2018 .

王晓丽 . 钓鱼邮件攻击防范指南 [J ] . 计算机与网络 , 2018 , 581 ( 13 ): 56 - 57 .

WANG X L . Guidelines for preventing phishing email attacks [J ] .Computer ＆ Network2018,581(13):56-57. 计算机与网络 , 2018 , 581 ( 13 ): 56 - 57 .

GASCON H , ULLRICH S , STRITTER B , et al . Reading between the lines:content-agnostic detection of spear-phishing emails [C ] // 21st RAID . 2018 .

SMADI S , ASLAM N , ZHANG L . Detection of online phishing email using dynamic evolving neural network based on reinforcement learning [J ] . Decision Support Systems , 2018 , 107 : 88 - 102 .

FANG Y , ZHANG C , HUANG C , et al . Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism [J ] . IEEE Access , 2019 , 7 : 56329 - 56340 .

高一男 , 蔡满春 . 基于 Hadoop 和 Mahout 的钓鱼邮件检测技术研究 [J ] . 电脑知识与技术 , 2016 , 12 ( 11 ): 27 - 30 .

GAO Y N , CAI M C . Research of phishing-mail detection based on Hadoop and Mahout [J ] . Computer Knowledge and Technology , 2016 , 12 ( 11 ): 27 - 30 .

AGGARWAL A , RAJADESINGAN A , KUMARAGURU P . PhishAri:automatic realtime phishing detection on Twitter [J ] . CoRR abs/1301.6899 , 2013

SHARMA N , SHARMA N , TIWARI V , et al . Real-time detection of phishing Tweets [C ] // The Fourth International Conference on Computer Science,Engineering and Applications . 2014 .

LIEW S W , SANI N F M , ABDULLAH M T , et al . An effective security alert mechanism for real-time phishing tweet detection on twitter [J ] . Computers ＆ Security , 2019

石春宏 . 移动终端如何识别钓鱼手段与防范 [J ] . 电脑知识与技术 , 2017 ( 31 ): 43 - 44 .

SHI C H . How to identify and prevent phishing on mobile termin-als [J ] . Computer Knowledge and Technology , 2017 ( 31 ): 43 - 44 .

BICAKCI K , UNAL D , ASCIOGLU N , et al . Mobile authentication secure against man-in-the-middle attacks [C ] // 2nd Mobile Cloud 2014 ,

GOEL D , JAIN A K . Mobile phishing attacks and defence mechanisms:state of art and open research challenges [J ] . Computers ＆Security , 2018 , 73 : 519 - 544 .

WU L , DU X , WU J . Effective defense schemes for phishing attacks on mobile computing platforms [J ] . IEEE Trans Vehicular Technology , 2016 , 65 ( 8 ): 6678 - 6691 .

VIRVILIS N , TSALIS N , MYLONAS A , et al . Mobile devices:a phisher′s paradise [C ] // 11th International Conference on Security and Cryptography . 2014 .

段青 . 一种移动平台钓鱼攻击的解决方法 [J ] . 信息安全与技术 , 2016 , 7 ( 4 ): 50 - 54 .

DUAN Q . A solution for phishing attackon Android platform [J ] . Cyberspace Security , 2016 , 7 ( 4 ): 50 - 54 .

NDIBWILE J D , KADOBAYASHI Y , FALL D . UnPhishMe:phishing attack detection by deceptive login simulation through an android mobile App [C ] // 12th AsiaJCIS . 2017 .

LIU D , LIU D , LI Y , et al . Efficient Android phishing detection based on improved naïve bayes algorithm [C ] // 10th ICSI . 2019 .

刘永明 , 杨婧 . 基于图像相似性的Android钓鱼恶意应用检测方法 [J ] . 计算机系统应用 , 2014 , 23 ( 12 ): 170 - 175 .

LIU Y M , YANG J . Detection of android phishing malwares based on image similarity [J ] . Computer Systems ＆ Applications , 2014 , 23 ( 12 ): 170 - 175 .

DING Y , LUKTARHAN N , LI K , et al . A keyword-based combination approach for detecting phishing webpages [J ] . Computers ＆Security , 2019 , 84 : 256 - 275 .

ADEBOWALE M A , LWIN K T,SÁNCHEZ E , et al . Intelligent web-phishing detection and protection scheme using integrated features of images,frames and text [J ] . Expert Syst Appl , 2019 , 115 : 300 - 313 .

ABUTAIR H , BELGHITH A , ALAHMADI S . CBR-PDS:a case-based reasoning phishing detection system [J ] . Journal of Ambient Intelligence and Humanized Computing , 2019 , 10 ( 7 ): 2593 - 2606 .

HE M , HORNG S J , FAN P , et al . An efficient phishing webpage detector [J ] . Expert Syst Appl , 2011 , 38 ( 10 ): 12018 - 12027 .

MOHAMMAD R M , THABTAH F , MCCLUSKEY L . An assessment of features related to phishing websites using an automated technique [C ] // 7th ICITST . 2012 .

FALCONIERI V . Open dataset of phishing and tor hidden services screen-captures [J ] . CoRR abs/1908.02449 , 2019

张茜 , 延志伟 , 李洪涛 , 等 . 网络钓鱼欺诈检测技术研究 [J ] . 网络与信息安全学报 , 2017 , 3 ( 7 ): 11 - 28 .

ZHANG X , YAN Z W , LI H T.et al . Research of phishing detection technology [J ] . Chinese Journal of Network and Information Secu-rity , 2017 , 3 ( 7 ): 11 - 28 .

SHIRAZI H , BEZAWADA B , RAY I , et al . Adversarial sampling attacks against phishing detection [M ] // Data and Applications Security and Privacy.Berlin:Springer . 2019 .

浏览量

1453

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

中文文本分类方法综述

DNS异常检测：大规模实验分析

基于代码相似性的软件漏洞检测研究进展

基于扩散模型的机器学习后门攻击防御方法

基于垂直分割的个人数据隐私保护方法