Abstract
The construction and interaction procedure of phishing and user in the deception mode is presented. We analyses phishing behavior when tempting human in order to construct a phishing-deception human-based data model (PDHDM) based on frequent associated events. The proposed phishing-deception human-based data model is utilized to generate association rules and to accurately classify between phishing and legitimate websites. This approach can reduce false positive rates in phishing detection systems, including a lack of effective dataset. Classification algorithms is employed for training and validation of the model. The proposed approach performance and the existing work is compared. Our proposed method yielded a remarkable result. The finding demonstrates that phishing-deception human-based data model is a promising scheme to develop effective phishing detection systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ead, W., Abdelwahed, W., Abdul-Kader, H.: Adaptive fuzzy classification- rule algorithm in detection malicious web sites from suspicious URLS. Int. Arab. J. eTechnol. 3(1), 1–9 (2013)
Dong, X., Clerk, J.A., Jacob, J.L.: Defending the weakest link: Phishing Website Detection by analysing User Behaviours. IEEE Telecommun. Syst. 45, 215–226 (2010)
RSA Anti-Fraud Command Center. www.rsa.com. Accessed 20 Sept 2013
Xiang, G., Hong, J.: A hybrid phish detection approach by identity discovery and keywords retrieval. In: Proceedings of the 18th International World Wide Web Conference (WWW 2009), pp. 571–580 (2009)
Jain, A., Richariya, V.: Implementing a web browser with phishing detection techniques. World Comput. Sci. Inf. Technol. J. (WCSIT) 1(7), 289–291 (2011)
Microsoft Corporation: Internet Explorer 7 (2014). http://www.microsoft.com/windows/ie/default.mspx. Accessed 9 Nov 2006
Netcraft. Netcraft Anti-Phishing Toolbar. http://toolbar.netcraft.com/. Accessed 13 June 2006
EarthLink Inc. EarthLink Toolbar (2014). http://www.earthlink.net/software/free/toolbar/. Accessed 9 Nov 2006
Xiang, G., Pendleton, B.A., Hong, J., Rose, C.P.: A hierarchical adaptive probabilistic approach for zero hour phish detection. In: Gritzalis, D., Preneel, B., Theoharidou, M. (eds.) ESORICS 2010. LNCS, vol. 6345, pp. 268–285. Springer, Heidelberg (2010)
Aburrous, M., Khelifi, A.: Phishing detection plug-in toolbar using intelligent fuzzy-classification mining techniques. Int. J. Soft Comput. Softwa. Eng. (SCSE 2013) 3 (2013). Special Issue
Barraclough, P.A., Hossain, M.A., Tahir, M.A., Sexton, G., Aslam, N.: Intelligent phishing detection and protection scheme for online transactions. Expert Syst. Appl. 40, 4697–4706 (2013)
Sheng, S., Wardman, B., Warner, G., Cranor, L., Hong, J., Zhang, C.: An empirical analysis of phishing blacklists. In: 6th Conference Proceeding, CEAS, Mountain View, Califonia, USA (2009)
Ma, J., Saul, L., Savag, S., Voelker, G.: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 1245–1254 (2009)
Afroz, A., Greenstadt, R.: PhishZoo: detecting phishing websites by looking at them. In: Proceedings of the IEEE Fifth International Conference on Semantic Computing (ICS 2011) (2011)
Aburrous, M., Khelifi, A.: Phishing detection plug-in toolbar using intelligent fuzzy-classification mining techniques. Int. J. Soft Comput. Softw. Eng. (JSCSE) 3, 54–61 (2013)
McAfee Inc. McAfee SiteAdvisor (2014). http://www.siteadvisor.com/. Accessed 9 Nov 2006
GeoTrust Inc. TrustWatch Toolbar (2014). http://toolbar.trustwatch.com/tour/v3ie/toolbar-v3ie-tour-overview.html. Accessed 13 June 2006
eBay Inc. Using eBay Toolbar’s Account Guard (2014). http://pages.eBay.com/help/confidence/account-guard.html. Accessed 13 June 2006
Millersmiles (2016). http://www.millersmiles.co.uk/. Accessed 20 Oct 2016
Huange, G.B., Zhu, Q.Y., Mao, K.Z., Siew, C.K., Saratchandran, P., Sundararajan, N.: Can threshold networks be trained directly. IEEE Trans. Circ. Syst. II 53(3), 187–191 (2006)
PhishTank: Join the fight against phishing (2013). http://www.phishtank.com/. Accessed 5 July 2013 and 10 July 2013
Laorden, C., Ugart-Pedrero, X., Santos, I., Sanz, B., Nieves, J., Alvarez, P., Bringas, G.: Study on the effectiveness of anomaly detection for spam filtering. Inf. Sci. 277, 421–444 (2014)
Ajlouni, M., Hadi, W., Alwedyan, J.: Detecting phishing websites using associative classification. Eur. J. Bus. Manage. 5(15), 36–40 (2013)
Abdelhamid, N., Ayesh, A., Thabtah, F.: Phishing detection based associative classification data mining. Expert Syst. Appl. 41(13), 5948–5959 (2014)
Rami, M., Thabtah, F.A., McCluskey, T.: Intelligent rule based phishing websites classification. IET Inf. Secur. 8(3), 153–160 (2014)
Agrawal, R., Ramakrishnan, S.: Fast algorithms for mining association rules in large database. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile, pp. 487–499 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Phishing Email Imitation
The email in Fig. 3 is an example of a phishing email that imitate Barclays bank legitimate email. It looks a genuine email with a link to illegitimate website. It also Copied contents of a legitimate website such as logos, text, images to make it look authentic, but in reality it is not legitimate.
Copy of a phishing email imitating legitimate Barclays bank email
Phishing Website Copy
The website in Fig. 4 is a phishing website that mimic Barclays bank legitimate website. It carries a forms to collect users bank detail in which this form is similar to that in the legitimate website, but in real it is not a legitimate form.
Phishing website imitating legitimate Barclays bank website.
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Barraclough, P., Sexton, G. (2016). Phishing-Deception Data Model for Online Detection and Human Protection. In: Jahankhani, H., et al. Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017. Communications in Computer and Information Science, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-319-51064-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-51064-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51063-7
Online ISBN: 978-3-319-51064-4
eBook Packages: Computer ScienceComputer Science (R0)