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PhishingWebsites

PhishingWebsites

active ARFF Publicly available Visibility: public Uploaded 16-02-2016 by Hilda Fabiola Bernard
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  • OpenML-CC18 OpenML100 study_123 study_14 study_144 study_34 study_52 study_98 study_99
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Author: Rami Mustafa A Mohammad ( University of Huddersfield","rami.mohammad '@' hud.ac.uk","rami.mustafa.a '@' gmail.com) Lee McCluskey (University of Huddersfield","t.l.mccluskey '@' hud.ac.uk ) Fadi Thabtah (Canadian University of Dubai","fadi '@' cud.ac.ae) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/phishing+websites) Please cite: Please refer to the [Machine Learning Repository's citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) Source: Rami Mustafa A Mohammad ( University of Huddersfield, rami.mohammad '@' hud.ac.uk, rami.mustafa.a '@' gmail.com) Lee McCluskey (University of Huddersfield,t.l.mccluskey '@' hud.ac.uk ) Fadi Thabtah (Canadian University of Dubai,fadi '@' cud.ac.ae) Data Set Information: One of the challenges faced by our research was the unavailability of reliable training datasets. In fact this challenge faces any researcher in the field. However, although plenty of articles about predicting phishing websites have been disseminated these days, no reliable training dataset has been published publically, may be because there is no agreement in literature on the definitive features that characterize phishing webpages, hence it is difficult to shape a dataset that covers all possible features. In this dataset, we shed light on the important features that have proved to be sound and effective in predicting phishing websites. In addition, we propose some new features. Attribute Information: For Further information about the features see the features file in the [data folder](https://archive.ics.uci.edu/ml/machine-learning-databases/00327/Phishing Websites Features.docx) of UCI. Relevant Papers: Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi (2012) An Assessment of Features Related to Phishing Websites using an Automated Technique. In: International Conferece For Internet Technology And Secured Transactions. ICITST 2012 . IEEE, London, UK, pp. 492-497. ISBN 978-1-4673-5325-0 Mohammad, Rami, Thabtah, Fadi Abdeljaber and McCluskey, T.L. (2014) Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25 (2). pp. 443-458. ISSN 0941-0643 Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2014) Intelligent Rule based Phishing Websites Classification. IET Information Security, 8 (3). pp. 153-160. ISSN 1751-8709 Citation Request: Please refer to the Machine Learning Repository's citation policy

31 features

Result (target)nominal2 unique values
0 missing
having_IP_Addressnominal2 unique values
0 missing
URL_Lengthnominal3 unique values
0 missing
Shortining_Servicenominal2 unique values
0 missing
having_At_Symbolnominal2 unique values
0 missing
double_slash_redirectingnominal2 unique values
0 missing
Prefix_Suffixnominal2 unique values
0 missing
having_Sub_Domainnominal3 unique values
0 missing
SSLfinal_Statenominal3 unique values
0 missing
Domain_registeration_lengthnominal2 unique values
0 missing
Faviconnominal2 unique values
0 missing
portnominal2 unique values
0 missing
HTTPS_tokennominal2 unique values
0 missing
Request_URLnominal2 unique values
0 missing
URL_of_Anchornominal3 unique values
0 missing
Links_in_tagsnominal3 unique values
0 missing
SFHnominal3 unique values
0 missing
Submitting_to_emailnominal2 unique values
0 missing
Abnormal_URLnominal2 unique values
0 missing
Redirectnominal2 unique values
0 missing
on_mouseovernominal2 unique values
0 missing
RightClicknominal2 unique values
0 missing
popUpWidnownominal2 unique values
0 missing
Iframenominal2 unique values
0 missing
age_of_domainnominal2 unique values
0 missing
DNSRecordnominal2 unique values
0 missing
web_trafficnominal3 unique values
0 missing
Page_Ranknominal2 unique values
0 missing
Google_Indexnominal2 unique values
0 missing
Links_pointing_to_pagenominal3 unique values
0 missing
Statistical_reportnominal2 unique values
0 missing

62 properties

11055
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
31
Number of nominal attributes.
74.19
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.58
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.5
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0.57
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
44.31
Percentage of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.44
Standard deviation of the number of distinct values among attributes of the nominal type.
0.83
Average entropy of the attributes.
4898
Number of instances belonging to the least frequent class.
0
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.
23
Number of binary attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.51
Average class difference between consecutive instances.
0.05
Average mutual information between the nominal attributes and the target attribute.
0.7
Second quartile (Median) of entropy among attributes.
0.99
Entropy of the target attribute values.
15.06
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2.26
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
19.06
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
55.69
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
6157
Number of instances belonging to the most frequent class.
0.26
Minimal entropy among attributes.

16 tasks

26039 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Result
17125 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Result
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Result
0 runs - estimation_procedure: 33% Holdout set - target_feature: Result
47 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Result
0 runs - target_feature: Result
1297 runs - target_feature: Result
1296 runs - target_feature: Result
0 runs - target_feature: Result
0 runs - target_feature: Result
0 runs - target_feature: Result
0 runs - target_feature: Result
0 runs - target_feature: Result
0 runs - target_feature: Result
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