Abstract
Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Users’ immense dependence on digital platform increases the chance of fraudulence. Phishing attacks are the most common ways of attack in the digital world. Any communication method can be used to target an individual and trick them into leaking confidential data in a fake environment, which can be later used to harm the sole victim or even an entire business depending on the attacker’s intend and the type of leaked data. Researchers have developed enormous anti-phishing tools and techniques like whitelist, blacklist, and antivirus software to detect web phishing. Classification is one of the techniques used to detect website phishing. This paper has proposed a model for detecting phishing attacks using various machine learning (ML) classifiers. K-nearest neighbors, random forest, support vector machines, and logistic regression are used as the machine learning classifiers to train the proposed model. The dataset in this research was obtained from the public online repository Mendeley with 48 features are extracted from 5000 phishing websites and 5000 real websites. The model was analyzed using F1 scores, where both precision and recall evaluations are taken into consideration. The proposed work has concluded that the random forest classifier has achieved the most efficient and highest performance scoring with 98% accuracy.
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
Similar content being viewed by others
References
Da Silva JAT, Al-Khatib A, Tsigaris P (2020) Spam e-mails in academia: issues and costs. Scientometrics 122:1171–1188
Mironova SM, Simonova SS (2020) Protection of the rights and freedoms of minors in the digital space. Russ J Criminol 14:234–241
Sethuraman SC, Vijayakumar V, Walczak S (2020) Cyber attacks on healthcare devices using unmanned aerial vehicles. J Med Syst 44:10
Tuan TA, Long HV, Son L, Kumar R, Priyadarshini I, Son NTK (2020) Performance evaluation of Botnet DDoS attack detection using machine learning. Evol Intell 13:283–294
Azeez NA, Salaudeen BB, Misra S, Damasevicius R, Maskeliunas R (2020) Identifying phishing attacks in communication networks using URL consistency features. Int J Electron Secur Digit Forensics 12:200–213
Iwendi C, Jalil Z, Javed AR, Reddy GT, Kaluri R, Srivastava G, Jo O (2020) KeySplitWatermark: zero watermarking algorithm for software protection against cyber-attacks. IEEE Access 8:72650–72660
Liu XW, Fu JM (2020) SPWalk: similar property oriented feature learning for phishing detection. IEEE Access 8:87031–87045
Parra GD, Rad P, Choo KKR, Beebe N (2020) Detecting internet of things attacks using distributed deep learning. J Netw Comput Appl 163:13
Tan CL, Chiew KL, Yong KSC, Sze SN, Abdullah J, Sebastian Y (2020) A graph-theoretic approach for the detection of phishing webpages. Comput Secur 95:14
Anwar S, Al-Obeidat F, Tubaishat A, Din S, Ahmad A, Khan FA, Jeon G, Loo J (2020) Countering malicious URLs in internet of things using a knowledge-based approach and a simulated expert. IEEE Internet Things J 7:4497–4504
Ariyadasa S, Fernando S, Fernando S (2020) Detecting phishing attacks using a combined model of LSTM and CNN. Int J Adv Appl Sci 7:56–67
Bozkir AS, Aydos M (2020) LogoSENSE: a companion HOG based logo detection scheme for phishing web page and E-mail brand recognition. Comput Secur 95:18
Gupta BB, Jain AK (2020) Phishing attack detection using a search engine and heuristics-based technique. J Inf Technol Res 13:94–109
Sonowal G, Kuppusamy KS (2020) PhiDMA—a phishing detection model with multi-filter approach. J King Saud Univ Comput Inf Sci 32:99–112
Zamir A, Khan HU, Iqbal T, Yousaf N, Aslam F, Anjum A, Hamdani M (2020) Phishing web site detection using diverse machine learning algorithms. Electron Libr 38:65–80
Rodriguez GE, Torres JG, Flores P, Benavides DE (2020) Cross-site scripting (XSS) attacks and mitigation: a survey. Comput Netw 166:23
Das A, Baki S, El Aassal A, Verma R, Dunbar A (2020) SoK: a comprehensive reexamination of phishing research from the security perspective. IEEE Commun Surv Tutor 22:671–708
Adewole KS, Hang T, Wu WQ, Songs HB, Sangaiah AK (2020) Twitter spam account detection based on clustering and classification methods. J Supercomput 76:4802–4837
Rao RS, Vaishnavi T, Pais AR (2020) CatchPhish: detection of phishing websites by inspecting URLs. J Ambient Intell Humaniz Comput 11:813–825
Shabudin S, Sani NS, Ariffin KAZ, Aliff M (2020) Feature selection for phishing website classification. Int J Adv Comput Sci Appl 11:587–595
Raja SE, Ravi R (2020) A performance analysis of software defined network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA). Comput Commun 153:375–381
Sarma D (2012) Security of hard disk encryption. Masters Thesis, Royal Institute of Technology, Stockholm, Sweden. Identifiers: urn:nbn:se:kth:diva-98673 (URN)
Alqahtani H et al (2020) Cyber intrusion detection using machine learning classification techniques. In: Computing science, communication and security, pp 121–31. Springer, Singapore
Hossain S, et al (2019) A belief rule based expert system to predict student performance under uncertainty. In: 2019 22nd international conference on computer and information technology (ICCIT), pp 1–6. IEEE
Ahmed F et al (2020) A combined belief rule based expert system to predict coronary artery disease. In: 2020 international conference on inventive computation technologies (ICICT), pp 252–257. IEEE
Hossain S et al (2020) A rule-based expert system to assess coronary artery disease under uncertainty. In: Computing science, communication and security, Singapore, pp 143–159. Springer, Singapore
Hossain S et al (2020) Crime prediction using spatio-temporal data. In: Computing science, communication and security. Springer, Singapore, pp 277–289
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarma, D., Mittra, T., Bawm, R.M., Sarwar, T., Lima, F.F., Hossain, S. (2021). Comparative Analysis of Machine Learning Algorithms for Phishing Website Detection. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 173. Springer, Singapore. https://doi.org/10.1007/978-981-33-4305-4_64
Download citation
DOI: https://doi.org/10.1007/978-981-33-4305-4_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4304-7
Online ISBN: 978-981-33-4305-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)