Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

Mustafa Aydin; Ismail Butun; Kemal Bicakci; Nazife Baykal

doi:10.1109/CCWC47524.2020.9031125

Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

Mustafa Aydin, Ismail Butun, Kemal Bicakci, Nazife Baykal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.

Original language	English
Title of host publication	2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020
Editors	Satyajit Chakrabarti, Rajashree Paul
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	774-779
Number of pages	6
ISBN (Electronic)	9781728137834
DOIs	https://doi.org/10.1109/CCWC47524.2020.9031125
Publication status	Published - Jan 2020
Externally published	Yes
Event	10th Annual Computing and Communication Workshop and Conference, CCWC 2020 - Las Vegas, United States Duration: 6 Jan 2020 → 8 Jan 2020

Publication series

Name	2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020

Conference

Conference	10th Annual Computing and Communication Workshop and Conference, CCWC 2020
Country/Territory	United States
City	Las Vegas
Period	6/01/20 → 8/01/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Funding

ACC : Overall Accuracy CAR : Cumulative Abnormal Return CCH : Contrast Context Histogram DOM : Document Object Model DM : Data Mining DT : Decision Tree FP : False Positive LR : Logistic Regression PII : Personal Identification Information MLP : Multi-Layer Perceptron NB : Naïve Bayes NN : Neural Network SVM : Support Vector Machines TP : True Positive TSVM: Transductive SVM WEKA: Waikato Environment for Knowledge Analysis ACKNOWLEDGEMENTS This research has been partially supported by the Swedish Civil Contingencies Agency (MSB) through the projects RICS, by the EU Horizon 2020 Framework Programme under grant agreement 773717, and by the STINT grant IB2019-8185.

Funders	Funder number
Horizon 2020 Framework Programme	773717
Swedish Foundation for International Cooperation in Research and Higher Education	IB2019-8185
Myndigheten för Samhällsskydd och Beredskap

Keywords

Attribute-based feature selection
Cyber theft
Data analysis
Fraudulent website detection
Machine learning algorithms

Access to Document

10.1109/CCWC47524.2020.9031125

Cite this

Aydin, M., Butun, I., Bicakci, K., & Baykal, N. (2020). Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. In S. Chakrabarti, & R. Paul (Eds.), 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020 (pp. 774-779). Article 9031125 (2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCWC47524.2020.9031125

Aydin, Mustafa ; Butun, Ismail ; Bicakci, Kemal et al. / Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020. editor / Satyajit Chakrabarti ; Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 774-779 (2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020).

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title = "Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs",

abstract = "Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Na{\"i}ve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Na{\"i}ve Bayes performed poor and is the least recommended method among all.",

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Aydin, M, Butun, I, Bicakci, K & Baykal, N 2020, Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. in S Chakrabarti & R Paul (eds), 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020., 9031125, 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 774-779, 10th Annual Computing and Communication Workshop and Conference, CCWC 2020, Las Vegas, United States, 6/01/20. https://doi.org/10.1109/CCWC47524.2020.9031125

Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. / Aydin, Mustafa; Butun, Ismail; Bicakci, Kemal et al.
2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020. ed. / Satyajit Chakrabarti; Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2020. p. 774-779 9031125 (2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2020/1

Y1 - 2020/1

N2 - Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.

AB - Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.

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Aydin M, Butun I, Bicakci K, Baykal N. Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. In Chakrabarti S, Paul R, editors, 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 774-779. 9031125. (2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020). doi: 10.1109/CCWC47524.2020.9031125

Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

Abstract

Publication series

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Bibliographical note

Funding

Keywords

Access to Document

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