Phishing E-mail Detection Method Based on Density and Distance

WANG Xiujuan; ZHANG Chenxi; TANG Haoyang; TAO Yuanrui

doi:10.11936/bjutxb2017110027

Journal of Beijing University of Technology > 2019 > 45(6): 546-553. > DOI: 10.11936/bjutxb2017110027

WANG Xiujuan, ZHANG Chenxi, TANG Haoyang, TAO Yuanrui. Phishing E-mail Detection Method Based on Density and Distance[J]. Journal of Beijing University of Technology, 2019, 45(6): 546-553. DOI: 10.11936/bjutxb2017110027

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Phishing E-mail Detection Method Based on Density and Distance

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

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Received Date: November 12, 2017
Available Online: August 03, 2022
Published Date: June 09, 2019

Graphical Abstract

Abstract

Abstract

Phishing E-mail detection methods are mostly focused on the extraction of different E-mail features, which lead the time increasing. To solve this problem, a method based on density and distance was proposed. The method replaces the 42 original mail features with 2 new ones, i.e., features based on density and distance. Then the machine learning classification algorithm was used to detect phishing E-mail. The detection accuracy of the proposed method reaches 99.74%, and time is only 3.39 s, which is 1/20 of the traditional algorithm. Results show that the algorithm has a better detection performance and saves much time.
- machine learning,
- phishing E-mail,
- feature extraction,
- dimensionality reduction,
- support vector machine (SVM)

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References (23)

References

[1]	中文互联网数据资讯中心. IDC: 预测2016年全球网民用户数达32亿人[R/OL].[2016-12-22]. http://www.199it.com/archives/422330.html.
[2]	CHOWDHURY M U, ABAWAJY J H, KELAREV A V, et al. Multilayer hybrid strategy for phishing email zero-day filtering[J]. Concurrency & Computation Practice & Experience, 2016, 29(23):623-639. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a0fcdd0be5b711eb4042f5d452c65c6a
[3]	杨明, 杜彦辉, 刘晓娟.网络钓鱼邮件分析系统的设计与实现[J].中国人民公安大学学报(自然科学版), 2012(72):214-226. http://d.old.wanfangdata.com.cn/Periodical/gadxxb-zrkxb201202014 YANG M, DU Y H, LIU X J. The design and implementation of phishing email analysis system[J]. Journal of Chinese People's Public Security University (Natural Science Edition), 2012(72):214-226. (in Chinese) http://d.old.wanfangdata.com.cn/Periodical/gadxxb-zrkxb201202014
[4]	中国反钓鱼联盟.中国反钓鱼联盟2016年11月月报[R/OL].[2016-12-22]. http://www.apac.cn/.
[5]	WU L, DU X, WU J. Effective defense schemes for phishing attacks on mobile computing platforms[J]. IEEE Transactions on Vehicular Technology, 2016, 65(8):6678-6691. doi: 10.1109/TVT.2015.2472993
[6]	CHOWDHURY M U, ABAWAJY J H, KELAREV A V, et al. Multilayer hybrid strategy for phishing email zero-day filtering[J]. Concurrency & Computation Practice & Experience, 2016, 29(23):56-74. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a0fcdd0be5b711eb4042f5d452c65c6a
[7]	PRAKASH P, KUMAR M, KOMPELLA R R, et al. Phishnet: predictive blacklisting to detect phishing attacks[C]//Proceedings of IEEE International Conference on Computer Communications. Washington DC: IEEE Computer Society, 2010: 1-5.
[8]	邹学强, 张鹏, 黄彩云, 等.基于页面布局相似性的钓鱼网页发现方法[J].通信学报, 2016(增刊1):116-124. http://www.cnki.com.cn/Article/CJFDTOTAL-TXXB2016S1018.htm ZOU X Q, ZHANG P, HUANG C Y, et al. Phishing Web page discovery method based on similarity of page layout[J]. Journal of Communication, 2016(Suppl 1):116-124. (in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-TXXB2016S1018.htm
[9]	VARSHNEY G, MISRA M, ATREY P K. A survey and classification of Web phishing detection schemes[J]. Security & Communication Networks, 2016, 9:6266-6284. http://cn.bing.com/academic/profile?id=077212eb8544aa106826e29e62e938a8&encoded=0&v=paper_preview&mkt=zh-cn
[10]	FETTE I, SADEH N, TOMASIC A. Learning to detect phishing emails[C]//International Conference on World Wide Web, WWW 2007. New York: ACM, 2007: 649-656.
[11]	KHONJI M, IRAQI Y, JONES A. Enhancing phishing e-mail classifiers:a lexical URL analysis approach[J]. International Journal to Information Security Research, 2013, 3(1):236-245. http://cn.bing.com/academic/profile?id=16f320eef7aee7a0a8b29f6947f583f6&encoded=0&v=paper_preview&mkt=zh-cn
[12]	IQBAL F, BINSALLEEH H, FUNG B C M, et al. Mining writeprints from anonymous e-mails for forensic investigation[J]. Digital Investigation, 2010, 7(1/2):56-64. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3dd1e4741e4572580db4d73661ec2382
[13]	潘锋.特征提取与特征选择技术研究[D].南京: 南京航空航天大学, 2011. PAN F. Research on feature extraction and feature selection[D]. Nanjing: Nanjing University of Aeronautics & Astronautics, 2011. (in Chinese)
[14]	TSAI C F, LIN C Y. A triangle area based nearest neighbors approach to intrusion detection[J]. Pattern Recognition, 2010, 43(1):222-229. doi: 10.1016/j.patcog.2009.05.017
[15]	TOOLAN F, CARTHY J. Feature selection for spam and phishing detection[C]//Ecrime Researchers Summit(Ecrime). Washington DC: IEEE Computer Society, 2010: 1-12.
[16]	ZAREAPOOR M, SHAMSOLMOALI P, ALAM M A. Highly discriminative features for phishing email classification by SVD[J]. Advances in Intelligent Systems & Computing, 2015, 339:649-656. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC0214849485
[17]	WANG S, WANG D, CAOYUAN L I, et al. Clustering by fast search and find of density peaks with data field[J]. Chinese Journal of Electronics, 2016, 25(3):397-402. doi: 10.1049/cje.2016.05.001
[18]	郑金彬, 卓义宝.基于密度的分布式聚类算法研究[J].计算机工程, 2008, 34(17):65-67. http://d.old.wanfangdata.com.cn/Periodical/jsjgc200817024 ZHENG J B, ZHUO Y B. Density based distributed clustering algorithm[J]. Computer Engineering, 2008, 34(17):65-67. (in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jsjgc200817024
[19]	LIN W C, KE S W, TSAI C F. CANN:an intrusion detection system based on combining cluster centers and nearest neighbors[J]. Knowledge-Based Systems, 2015, 78(1):13-21. http://d.old.wanfangdata.com.cn/Periodical/nmglydcsj200101021
[20]	马萌.基于流形距离的聚类算法研究及其应用[D].西安: 西安电子科技大学, 2009. MA M. Research and application of clustering algorithm based on manifold distance[D]. Xi'an: Xi'an Electronic and Science University, 2009. (in Chinese)
[21]	WANG X J, ZHAN C X, ZHENG K F. Intrusion detection algorithm based on density, cluster centers, and nearest neighbors[J]. China Communications, 2016, 13(7):24-31. doi: 10.1109/CC.2016.7559072
[22]	DEVARAJU S, RAMAKRISHNAN S. Detection of attacks for IDS using association rule mining algorithm[J]. Iete Journal of Research, 2015, 61(6):624-633. doi: 10.1080/03772063.2015.1034197
[23]	WANG F N. Solving the intrusion detection problem with KPCA-RVM[C]//Design, Manufacturing and Mechatronics. Singapore: World Scientific, 2015: 520-527.

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Phishing E-mail Detection Method Based on Density and Distance