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

Banking Regulation and Supervision Agency
Chalmers University of Technology
TOBB University of Economics and Technology
Middle East Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakemli

16 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020
Editörler	Satyajit Chakrabarti, Rajashree Paul
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	774-779
Sayfa sayısı	6
ISBN (Elektronik)	9781728137834
DOI'lar	https://doi.org/10.1109/CCWC47524.2020.9031125
Yayın durumu	Yayınlandı - Oca 2020
Harici olarak yayınlandı	Evet
Etkinlik	10th Annual Computing and Communication Workshop and Conference, CCWC 2020 - Las Vegas, United States Süre: 6 Oca 2020 → 8 Oca 2020

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	10th Annual Computing and Communication Workshop and Conference, CCWC 2020
Ülke/Bölge	United States
Şehir	Las Vegas
Periyot	6/01/20 → 8/01/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Finansman

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.

Finansörler	Finansör numarası
Horizon 2020 Framework Programme	773717
Swedish Foundation for International Cooperation in Research and Higher Education	IB2019-8185
Myndigheten för Samhällsskydd och Beredskap

Belgeye Erişim

10.1109/CCWC47524.2020.9031125

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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). Makale 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. editör / 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).

@inproceedings{256f13d196b149679a7e608acaf812e8,

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.",

keywords = "Attribute-based feature selection, Cyber theft, Data analysis, Fraudulent website detection, Machine learning algorithms",

author = "Mustafa Aydin and Ismail Butun and Kemal Bicakci and Nazife Baykal",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 10th Annual Computing and Communication Workshop and Conference, CCWC 2020 ; Conference date: 06-01-2020 Through 08-01-2020",

year = "2020",

month = jan,

doi = "10.1109/CCWC47524.2020.9031125",

language = "English",

series = "2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "774--779",

editor = "Satyajit Chakrabarti and Rajashree Paul",

booktitle = "2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",

address = "United States",

}

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).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakemli

TY - GEN

T1 - Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

AU - Aydin, Mustafa

AU - Butun, Ismail

AU - Bicakci, Kemal

AU - Baykal, Nazife

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.

KW - Attribute-based feature selection

KW - Cyber theft

KW - Data analysis

KW - Fraudulent website detection

KW - Machine learning algorithms

UR - https://www.scopus.com/pages/publications/85083083149

U2 - 10.1109/CCWC47524.2020.9031125

DO - 10.1109/CCWC47524.2020.9031125

M3 - Conference contribution

AN - SCOPUS:85083083149

T3 - 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020

SP - 774

EP - 779

BT - 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020

A2 - Chakrabarti, Satyajit

A2 - Paul, Rajashree

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th Annual Computing and Communication Workshop and Conference, CCWC 2020

Y2 - 6 January 2020 through 8 January 2020

ER -

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, editörler, 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap