An Optimized Stacking Ensemble Model for Phishing Websites Detection
Abstract
:1. Introduction
2. Related Works
2.1. Recognizing Phishing Attacks in the IoT
2.2. Machine-Learning-Based Detection Methods
3. Materials and Methods
The Dataset and Experimental Design
4. Results and Discussion
4.1. Experimental Results of the Ensemble Classifiers without Optimization
4.2. Experimental Results of the GA-Based Ensemble Classifiers
4.3. Statistical Analysis and Comparison with Previous Studies
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Category | Feature Name | Description | Python Library Used |
---|---|---|---|
Address-bar-based | having_IP_Address | Using the IP Address | IPaddress Urllib Re Datetime BeautifulSoup Socket |
URL_Length | Long URL to hide the suspicious part | ||
Shortening_Service | Using shortening service | ||
having_At_Symbol | URL having @ symbol | ||
double_slash_redirecting | URL uses “//” symbol | ||
Prefix_Suffix | Add prefix or suffix separated by (-) | ||
having_Sub_Domain | Website has subdomain or multi-subdomain | ||
SSLfinal_State | Age of SSL certificate | ||
Domain_registeration_length | Domain registration length | ||
Favicon | Associated graphic image (icon) with webpage | ||
Port | Open port | ||
HTTPS_token | Presence of HTTP/HTTPS in domain name | ||
HTML- and JavaScript-based | Redirect | How many times a website has been redirected | Request BeautifulSoup |
on_mouseover | Effect of mouse over on status bar | ||
RightClick | Disabling right click | ||
popUpWindow | Using pop-up window to submit personal information | ||
Iframe | Using Iframe | ||
Abnormality based | Request_URL | % of external objects contained within a webpage | BeautifulSoup Re WHOIS |
URL_of_Anchor | % of URL Anchor (<a> tag) | ||
Links_in_tags | % of links in <meta>, <script> and <link> | ||
SFH | Server from Handler | ||
Submitting_to_email | Submit user information using mail or mailto | ||
Abnormal_URL | Host name in URL | ||
Domain-based features | age_of_domain | Age of the website | WHOIS Urllib BeautifulSoup |
DNSRecord | Website in WHOIS dataset | ||
web_traffic | Popularity of the website | ||
Page_Rank | Page Rank | ||
Google_Index | Google Index | ||
Links_pointing_to_page | # of links pointing to page | ||
Statistical_report’ | found in statistical reports | ||
Result | Website is classified as phishing or legitimate |
Measure | Random Forests (%) | AdaBoost (%) | XGBoost (%) | Bagging (%) | GradientBoost (%) | LightGBM (%) |
---|---|---|---|---|---|---|
Accuracy | 97.02 | 93.17 | 94.45 | 96.73 | 94.61 | 96.53 |
Precision | 96.58 | 94.70 | 94.52 | 94.99 | 94.87 | 95.15 |
Recall | 98.08 | 96.60 | 96.39 | 96.73 | 96.59 | 96.70 |
F-Score | 97.49 | 95.71 | 95.50 | 95.90 | 95.76 | 95.95 |
Measure | Random Forests (%) | AdaBoost (%) | XGBoost (%) | Bagging (%) | GradientBoost (%) | LightGBM (%) |
---|---|---|---|---|---|---|
Accuracy | 98.37 | 96.88 | 97.70 | 97.51 | 97.67 | 98.65 |
Precision | 98.54 | 96.74 | 97.57 | 97.55 | 97.58 | 98.56 |
Recall | 98.26 | 97.04 | 97.85 | 97.44 | 97.76 | 98.74 |
F-Score | 98.39 | 96.88 | 97.71 | 97.46 | 97.67 | 98.65 |
Measure | Random Forests (%) | AdaBoost (%) | XGBoost (%) | Bagging (%) | GradientBoost (%) | LightGBM (%) |
---|---|---|---|---|---|---|
Accuracy | 97.15 | 93.58 | 95.33 | 96.78 | 95.37 | 96.67 |
Precision | 95.78 | 90.89 | 92.75 | 95.80 | 93.06 | 95.08 |
Recall | 96.13 | 90.52 | 93.82 | 94.93 | 93.58 | 95.28 |
F-Score | 95.90 | 90.70 | 93.28 | 95.33 | 93.32 | 95.18 |
Parameter | Value |
---|---|
Generations | 10 |
Population size | 24 |
Mutation rate | 0.02 |
Crossover rate | 0.5 |
Early stop | 12 |
Classifier Name | Adjusted Parameters | Best GA-Based Configuration |
---|---|---|
Random Forests | Criterion: [‘entropy’, ‘gini’] max_depth: [10–1200] + [None] max_features: [‘auto’, ‘sqrt’,’log2’, None] min_samples_leaf: [4–12] min_samples_split: [5–10] n_estimators’: [150–1200] | Criterion: entropy max_depth: 142 min_samples_leaf: 4 min_samples_split: 5 n_estimators: 1200 |
AdaBoost | n_estimators: [100–1200] learning_rate: [1 × 10−3, 1 × 10−2, 1 × 10−1, 0.5, 1.0] | learning_rate: 0.1 n_estimators: 711 |
XGBoost | n_estimators: [100–1200] max_depth: [1–11], learning_rate: [1 × 10−3, 1 × 10−2, 1 × 10−1, 0.5, 1.] subsample: [0.05–1.01] min_child_weight: [1–21] | learning_rate: 0.1 max_depth: 5 min_child_weight: 3.0 n_estimators: 588 subsample: 0.7 |
Bagging | n_estimators: [100–1200] max_samples: [0.1, 0.2, 0.3, 0.4, 0.5, 1.0, 1.1] bootstrap: [True, False] | n_estimators: 1077 max_samples: 0.5 bootstrap: True |
GradientBoost | n_estimators: [100–1200] learning_rate: [1 × 10−3, 1 × 10−2, 1 × 10−1, 0.5, 1.0] subsample: [0.05–1.01] max_depth: [10–1200] + None min_samples_split: [5–10] min_samples_leaf: [4–12] max_features: [‘auto’, ‘sqrt’,’log2’, None] | n_estimators: 344 learning_rate: 1.0 subsample: 1.0 max_depth: 1067 min_samples_split: 5 min_samples_leaf: 12 max_features: ‘auto’ |
LightGBM | boosting_type: [‘gbdt’, ‘dart’, ‘goss’, ‘rf’]num_leaves: [5–42] max_depth: [10–1200] + None learning_rate: [1 × 10−3, 1 × 10−2, 1 × 10−1, 0.5, 1.] n_estimators: [100–1200] min_child_samples: [100,500] min_child_weight: [1 × 10−5, 1 × 10−3, 1 × 10−2, 1 × 10−1, 1, 10, 100, 1000, 10000] subsample: sp_uniform(loc = 0.2, scale = 0.8) colsample_bytree’: sp_uniform(loc = 0.4, scale = 0.6) reg_alpha: [0, 10−1, 1, 2, 5, 7, 10, 50, 100], reg_lambda: [0, 10−1, 1, 5, 10, 20, 50, 100], min_split_gain: 0.0, subsample_for_bin: 200,000 | boosting_type: ‘gbdt’ num_leaves: 13 max_depth: 15 learning_rate: 0.5 n_estimators: 500 min_child_samples: 399 min_child_weight: 0.1 subsample: 0.855 colsample_bytree: 0.9234 reg_alpha: 2 reg_lambda: 5 min_split_gain: 0.0, subsample_for_bin: 200,000 |
Fold | GA–RF (%) | GA–AdaBoost (%) | GA–XGBoost (%) | GA–Bagging (%) | GA–GradientBoost (%) | GA–LightGBM (%) |
---|---|---|---|---|---|---|
1 | 97.11 | 94.85 | 96.75 | 96.56 | 97.11 | 96.84 |
2 | 96.84 | 93.13 | 97.02 | 96.75 | 96.93 | 96.47 |
3 | 97.20 | 93.04 | 96.93 | 96.56 | 96.93 | 95.66 |
4 | 96.02 | 93.76 | 97.47 | 97.65 | 97.83 | 96.20 |
5 | 96.29 | 92.95 | 97.02 | 97.02 | 97.02 | 96.20 |
6 | 96.47 | 93.57 | 96.92 | 96.74 | 97.01 | 96.20 |
7 | 96.74 | 92.85 | 97.29 | 97.01 | 97.29 | 96.83 |
8 | 97.83 | 95.66 | 97.56 | 97.47 | 97.83 | 97.47 |
9 | 97.01 | 92.85 | 97.29 | 97.01 | 97.19 | 96.56 |
10 | 95.93 | 93.67 | 95.93 | 96.20 | 96.11 | 95.75 |
Average | 96.74 | 93.63 | 97.01 | 96.90 | 97.13 | 96.42 |
Classifier | Class Name | Precision (%) | Recall (%) | F-Score (%) |
---|---|---|---|---|
GA–Random Forests | Phishing website | 96.40 | 94.10 | 95.10 |
Legitimate | 95.20 | 97.30 | 96.40 | |
Weighted Average | 95.90 | 95.70 | 95.90 | |
GA–XGBoost | Phishing website | 97.50 | 95.80 | 96.50 |
Legitimate | 96.70 | 98.00 | 97.20 | |
Weighted Average | 97.00 | 97.00 | 97.00 | |
GA–GradientBoost | Phishing website | 97.00 | 95.70 | 96.40 |
Legitimate | 96.80 | 97.50 | 97.10 | |
Weighted Average | 96.90 | 96.80 | 96.80 | |
GA–LightGBM | Phishing website | 95.10 | 94.20 | 94.70 |
Legitimate | 95.50 | 96.30 | 95.80 | |
Weighted Average | 95.30 | 95.30 | 95.30 |
Measure | GA–Random Forests (%) | GA–AdaBoost (%) | GA–XGBoost (%) | GA–Bagging (%) | GA–GradientBoost (%) | GA–LightGBM (%) |
---|---|---|---|---|---|---|
Accuracy | 98.39 | 97.21 | 98.57 | 97.51 | 98.50 | 98.32 |
Precision | 98.46 | 97.15 | 98.50 | 97.24 | 98.31 | 98.10 |
Recall | 98.13 | 97.28 | 98.64 | 97.89 | 98.54 | 98.56 |
F-Score | 98.43 | 97.21 | 98.57 | 97.52 | 98.37 | 98.33 |
Measure | GA–Random Forests (%) | GA–AdaBoost (%) | GA–XGBoost (%) | GA–Bagging (%) | GA–GradientBoost (%) | GA–LightGBM (%) |
---|---|---|---|---|---|---|
Accuracy | 96.44 | 94.06 | 97.35 | 96.96 | 97.27 | 97.21 |
Precision | 94.63 | 90.91 | 96.20 | 95.3 | 95.68 | 96.13 |
Recall | 95.08 | 92.02 | 96.14 | 95.96 | 96.30 | 95.81 |
F-Score | 94.86 | 91.46 | 96.17 | 95.64 | 95.78 | 95.96 |
ML Classifier | Dataset 1 | Dataset 2 | Dataset 3 | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean Rank | Mean | SD | Mean Rank | Mean | SD | Mean Rank | Mean | SD | |
RF | 3.2 | 0.970 | 0.00427 | 4.2 | 0.9837 | 0.00332 | 3.6 | 0.971 | 0.00229 |
GA–RF | 4.4 | 0.967 | 0.00554 | 4.1 | 0.9839 | 0.00327 | 8 | 0.964 | 0.00269 |
AdaB | 11.7 | 0.932 | 0.00549 | 11.5 | 0.9688 | 0.00477 | 12 | 0.936 | 0.00335 |
GA–AdaB | 11.2 | 0.936 | 0.00889 | 10.2 | 0.9721 | 0.00448 | 11 | 0.941 | 0.00299 |
XGB | 9.6 | 0.945 | 0.00491 | 7.4 | 0.9770 | 0.00508 | 9.5 | 0.9532 | 0.00339 |
GA–XGB | 3.1 | 0.970 | 0.00447 | 2.5 | 0.9857 | 0.00310 | 1.5 | 0.974 | 0.00201 |
Bagging | 5.3 | 0.967 | 0.00492 | 8.9 | 0.9751 | 0.00567 | 5.8 | 0.968 | 0.00214 |
GA–Bagging | 4.3 | 0.969 | 0.00412 | 8.1 | 0.9751 | 0.00579 | 4.9 | 0.969 | 0.00243 |
GB | 9.2 | 0.946 | 0.00578 | 7.8 | 0.9767 | 0.00492 | 9.5 | 0.954 | 0.00330 |
GA–GB | 1.8 | 0.971 | 0.00464 | 2.9 | 0.9850 | 0.00293 | 2.2 | 0.973 | 0.00222 |
LGB | 6.1 | 0.965 | 0.00561 | 1.9 | 0.9865 | 0.00307 | 6.7 | 0.967 | 0.00227 |
GA–LGB | 6.7 | 0.964 | 0.00514 | 4.5 | 0.9832 | 0.00421 | 2.9 | 0.972 | 0.00235 |
Dataset | RF Level (%) | GB (%) | SVM (%) |
---|---|---|---|
Dataset 1 | 97.00 | 96.82 | 97.16 |
Dataset 2 | 98.57 | 98.47 | 98.58 |
Dataset 3 | 97.22 | 97.32 | 97.39 |
Classifier Name | Without Optimization | With GA Optimization | |
---|---|---|---|
Random Forests | Avg. | 97.02% | 96.74% |
Variance | 0.000 | 0.000 | |
AdaBoost | Avg. | 93.17% | 93.63% |
Variance | 0.000 | 0.000 | |
XGBoost | Avg. | 94.45% | 97.01% |
Variance | 0.000 | 0.000 | |
Bagging | Avg. | 96.73% | 96.90% |
Variance | 0.000 | 0.000 | |
GradientBoost | Avg. | 94.61 | 97.13% |
Variance | 0.000 | 0.000 | |
LightGBM | Avg. | 96.53% | 96.42% |
Variance | 0.000 | 0.000 |
Classifier Name | t-Test Result | Conclusion | |
---|---|---|---|
Random Forests | t-stat. | 1.466706885 | No significant |
p-value | 0.088 | improvement | |
AdaBoost | t-stat. | −2.100040666 | Significant |
p-value | 0.032556993 | improvement | |
XGBoost | t-stat. | −13.49130461 | Significant |
p-value | 0.000 | improvement | |
Bagging | t-stat. | −2.976672182 | Significant |
p-value | 0.008 | improvement | |
GradientBoost | t-stat. | −11.26647694 | Significant |
p-value | 0.000 | improvement | |
LightGBM | t-stat. | 0.971025 | No significant |
p-value | 0.178454 | improvement |
Paper | Classifier | Dataset | Accuracy% | Precision % | Recall % |
---|---|---|---|---|---|
Ali and Ahmed [7] | GA–ANN | Dataset 1 | 88.77 | 85.81% | 93.34% |
Ali and Malebary [10] | POS–RF | Dataset 1 | 96.83 | 98.76% | 95.37% |
This study | The stacking ensemble method | Dataset 1 | 97.16 | 96.86% | 96.83% |
Khan, Khan, and Hussain [35] | ANN after PCA | Dataset 2 | 97.13 | 96.48% | 98.03% |
This study | The stacking ensemble method | Dataset 2 | 98.58 | 98.50% | 98.74% |
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Al-Sarem, M.; Saeed, F.; Al-Mekhlafi, Z.G.; Mohammed, B.A.; Al-Hadhrami, T.; Alshammari, M.T.; Alreshidi, A.; Alshammari, T.S. An Optimized Stacking Ensemble Model for Phishing Websites Detection. Electronics 2021, 10, 1285. https://doi.org/10.3390/electronics10111285
Al-Sarem M, Saeed F, Al-Mekhlafi ZG, Mohammed BA, Al-Hadhrami T, Alshammari MT, Alreshidi A, Alshammari TS. An Optimized Stacking Ensemble Model for Phishing Websites Detection. Electronics. 2021; 10(11):1285. https://doi.org/10.3390/electronics10111285
Chicago/Turabian StyleAl-Sarem, Mohammed, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, and Talal Sarheed Alshammari. 2021. "An Optimized Stacking Ensemble Model for Phishing Websites Detection" Electronics 10, no. 11: 1285. https://doi.org/10.3390/electronics10111285