Abstract
Phishing attack is used to obtain the information like username, password, bank account details, and credit card details. It is a most popular cybercrime today. Phishing attacks also affect the online payment sector financial institution, file hosting or cloud storage, and many others. Phishing attack always targets to these Web sites which are related to the online payment sector and Web mail. Many techniques are used to prevent the phishing attack like blacklist, Heuristic, visual similarity, and machine learning. Blacklist technique is most commonly used because it is easy to implement, but this technique cannot detect a new phishing attack. So, now, machine learning is most efficient technique to detect the phishing attack and this technique is able to detect all drawback of other phishing detect techniques. So this research work is completely based on machine learning algorithms which are logistic regression, decision tree, random forest, and SVM to detect phishing Web site. In this research work, select the best model on the basis of six analysis factors.
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
APWG Report 1. http://docs.apwg.org/reports/apwg_trends_report_q2_2018.pdf
Phishing definition. https://en.wikipedia.org/wiki/Phishing
Rao RS, Pais AR (2018) Detection of phishing websites using an efficient feature-based machine learning framework. Springer
Liu C, Lang B (2018) Finding effective classifier for malicious URL detection. ACM, VNO-5
Gautam S, Rani K, Joshi B (2018) Detecting phishing websites using rule-based classification algorithm: a comparison. Springer
Pujara P, Chaudhari MB (2018) Phishing website detection using machine learning: a review. In: 2018 IJSRCSEIT, vol 3, issue no 7. ISSN 2456-3307
Mohammad RM, Thabtah F, McCluskey L (2012) An assessment of features related to phishing websites using an automated technique. In: International conference for internet technology and secured transactions (ICITS). IEEE
Abunadi A, Akanbi O, Zainal A (2013) Feature extraction process: a phishing detection approach. IEEE
Sampat H, Saharkar M, Pandey A, Lopes H (2018) Detection of phishing website using machine learning. Int Res J Eng Technol (IRJET) 5(3). E-ISSN 2395-0056. P-ISSN 2395-0072. www.irjet.net
Ali W (2017) Phishing website detection based on supervised machine learning with wrapper features selection. IJACSA 8
Naveen INVD, Manamohana K, Verma R (2019) Detection of malicious URLs using machine learning techniques. IJITEE 8. ISSN 2278-3075
Tahir MAUH, Asghar S, Zafar A, Gillani S (2016) A hybrid model to detect phishing-sites using supervised learning algorithms. In: International conference on computational science and computational intelligence. IEEE
Shirazi H, Haefner K, Ray I (2017) Fresh-phish: a framework for auto-detection of phishing websites. In: International conference on information reuse and integration (IRI), IEEE
Jain AK, Gupta BB (2017) Towards detection of phishing websites on client-side using machine learning based approach. IEEE
Sananse BE, Sarode TK (2015) Phishing URL detection: a machine learning and web mining-based approach. In: Int J Comput Appl
Mustafa AYDIN, Nazife BAYKAL (2015) Feature extraction and classification phishing websites based on URL. IEEE
Singh P, Maravi YP, Sharma S (2015) Phishing websites detection through supervised learning networks. IEEE
Pradeepthi. KV, Kannan A (2014) Performance study of classification techniques for phishing URL detection. In: 2014 (ICoAC) IEEE, vol 2
Nguyen LAT, To BL, Nguyen HK, Nguyen, MH (2013) Detecting phishing web sites: a heuristic URL-based approach. In: The 2013 international conference on advanced technologies for communications (ATC’13)
Abunadi A, Zainal A, Akanb O (2013) Feature extraction process: a phishing detection approach. IEEE
Mohammad RM, Thabtah F, McCluskey L (2012) An assessment of features related to phishing websites using an automated technique. In: The 7th international conference for internet technology and secured transactions, IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Preeti, Nandal, R., Joshi, K. (2021). Phishing URL Detection Using Machine Learning. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_42
Download citation
DOI: https://doi.org/10.1007/978-981-15-5341-7_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5340-0
Online ISBN: 978-981-15-5341-7
eBook Packages: EngineeringEngineering (R0)