Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek

Joy Agboi 1,* , Frances Uche Emordi 2 , Christopher Chukufunaya Odiakaose 3 , Rebecca Okeoghene Idama 4 ,
Evans Fubara Jumbo 5 , Amanda Enaodona Oweimieotu 6 , Peace Oguguo Ezzeh 7 , Andrew Okonji Eboka 8 ,
Anne Odoh 9 , Eferhire Valentine Ugbotu 10 , Paul Avwerosuo Onoma 11 , Arnold Adimabua Ojugo 12 ,
Tabitha Chukwudi Aghaunor 13 , Amaka Patience Binitie 14 , Christopher Chukwudi Onochie 15 ,
Blessing Uche Nwozor 16 , Patrick Ogholuwarami Ejeh 17

1 Faculty of Science, Delta State University, Abraka, Delta State, Nigeria

2, 3, 17 Faculty of Computing, Dennis Osadebay University, Asaba, Delta State, Nigeria

4 Faculty of Computing, Southern Delta University, Ozoro, Delta State, Nigeria

5, 6 School of Sciences, Edwin Clark University, Kiagbodo, Delta State, Nigeria

7, 8, 14, 15 School of Science, Federal College of Education (Technical), Asaba, Nigeria

9 School of Media and Communications, Pan-Atlantic University, Lekki, Lagos State, Nigeria

10 Department of Data Science, University of Salford, Manchester, United Kingdom

11, 12, 16 College of Computing, Federal University of Petroleum Resources, Effurun, Nigeria

13 Department of Data Intelligence and Tech, Robert Morris University, Pittsburgh, Pennsylvania, USA

Email: 1 agboijoy0@gmail.com, 2 emordi.frances@dou.edu.ng, 3 osegalaxy@gmail.com, 4 idamaro@dsust.edu.ng,
5 evans3447@gmail.com, 6 oweimieotuamanda@edwinclarkuniversity.edu.ng, 7 peace.ezzeh@fcetasaba.edu.ng,
8 ebokaandrew@gmail.com, 9 aodoh@pau.edu.ng, 10 eferhire.ugbotu@gmail.com, 11 kenbridge14@gmail.com,
12 ojugo.arnold@fupre.edu.ng, 13 tabitha.aghaunor@gmail.com, 14 amaka.binitie@fcetasaba.edu.ng,
15 xtoline2@gmail.com, 16 nwozor.blessing@fupre.edu.ng, 17 patrick.ejeh@dou.edu.ng 

*Corresponding Author

Abstract—The widespread proliferation of smartphones has advanced portability, data access ease, mobility, and other merits; it has also birthed adversarial targeting of network resources that seek to compromise unsuspecting user devices. Increased susceptibility was traced to user's personality, which renders them repeatedly vulnerable to exploits. Our study posits a stacked learning model to classify malicious lures used by adversaries on phishing websites. Our hybrid fuses 3-base learners (i.e. Genetic Algorithm, Random Forest, Modular Net) with its output sent as input to the XGBoost. The imbalanced dataset was resolved via SMOTE-Tomek with predictors selected using a relief rank feature selection. Our hybrid yields F1 0.995, Accuracy 1.000, Recall 0.998, Precision 1.000, MCC 1.000, and Specificity 1.000 – to accurately classify all 3,316 cases of its held-out test dataset. Results affirm that it outperformed benchmark ensembles. The study shows that our proposed model, as explored on the UCI Phishing Website dataset, effectively classified phishing (cues and lures) contents on websites.

Keywords—Phishing Website; SMOTE-Tomek; Data Balancing; Memetic Algorithm; Tree-based Ensembles

1. Introduction

Digital revolution has ushered in a plethora of tools and processes that seek to advance efficient knowledge exchange among users [1]. The devices ease data processing tasks [2] while offering the benefits of flexibility in the shared resource cum enhanced user-connectivity [3]. With security a  major issue, such advances have continued to ignite the interest of adversaries [4]. The proliferation of smartphones with their processing capacities has further eased it as invasive targets, with protocols made more possible with emergent tools [5], [6]. An adversary uses the penetrative tools like malware (spam) [7] to bolster socially-engineered threats that explore subterfuge mode to coordinate their attack at unsuspecting devices in their bid to compromise network infrastructure and resources [8][9]. These attack ensures that data exchange is targeted at exploits on a user’s social needs, desires [10] and insatiable traits [11]. Today’s businesses are reshaped via fusion of informatics [12] – as a channel to deliver high-end values to consumers, who receive services as rendered [13]. This exchange has today become a trillion-dollar war [14], as businesses must seek new frontiers to curb attacks amongst other issues [15][16], as failure to safeguard these exchanges ushers in the need for cross-cutting research [17][18].

The success of many of these adversarial attacks is hinged on user personality traits, which include online presence, emotional seclusion, insatiable wants, and trust issues [19]. An adversary masks their intent as a trusted ally, to exploit a compromised resource – providing the attacker with a pivot access for further exploits on the infrastructure [20]. The consequent rise in the adoption of smartphones has further eased these attacks and compromises considerably. Phishing simply redirects a user’s request to a spoofed website, rippled with malicious content that seeks to expose a targeted user [21] or device without their knowledge [22]. Phishing consist of 3-elements: (a) a lure masks an attacker as a genuine-user, targeting a user’s empathy, fear and curiosity [23], (b) a hook is an embedded link in a message [24], and (c) a catch obtains an exposed device’s private data [25]. Its success is hinged on its frequency and diversity [26] with unrealistic demands that seek to intimidate a user’s psyche with petty gifts [27], [28]. Vulnerability to scam can be due to demographics (i.e. age, gender, and status) shown as in Fig 1 to Fig. 3 [29][30]. Girls between 24-to-42 years were the most phished due to media presence or social seclusion [31]; There was also the factor of educational status cum societal approval [32]; and users between 18-to-29years were also phished more due to behavioural traits [33][34].

Victimization impacts website’s contents and its structure with greater probability an unsuspecting user will fall prey [35]. To identify malicious contents, we must eliminate gaps by [36]: (a) identify lures that increases believability in a user [37], and (b) assess the undetectability and potency of cues to unsuspecting users [38]. Learning models are successfully used to identify attacks, and detect cues and lures [39][40] that leave users as susceptible. They identify data anomalies via learned outliers in a dataset [41] as accomplished via vote, bagging, boosting, and stacked models/schemes [42][43].

MLs are veritable tools to identify attacks [44]. A trained MLs can detect anomalous patterns – even with its dynamic predictors [45]. Learning schemes are grouped into: machine learning (ML) [46][47], deep learning (DL) [48][49], and ensemble learning (EL) [50]. ML's flexibility and robustness help it to learn intrinsic patterns and decode predictors that fastens model design, and ease outliers identification [51]. Its pitfalls are imbalanced dataset and the feature selection mode used [52][53]. DLs utilize recurrent neural networks to capture chaotic, high-dimensional data patterns [54][55].

Its poor generalization due to the vanishing gradient problem, restricts its use. But, its variant overcomes this via its gates to control its input, and eases its adaptability to learned changes as long-term dependency [56]. Its inability to handle larger dataset and longer training time required implies the quest for better alternative [57]. Lastly, ELs effectively fuses ML with DL into a stronger learner to enhance performance [58]. It must resolve conflicts of structure and data-encode [59]; while, leveraging the merits of both ML and DL to avoid model overfit as birthed by the underlying models [60][61].

Thus, we explore the XGBoost to achieve such predictive abilities, leveraging its base, weak learners to enhance itself [62]. It will improve its performance via error reduction on its weak (base) learner, and reduce its overall variance and bias in the dataset to improve generalization. It benefits from the comprehensive knowledge of its weaker base learners, to improve its generalization by exploiting the XGBoost scheme [63]. With degraded performance due to an imbalanced data [64][65], we explore the variant SMOTE-Tomek balancing. Our study wishes to [66]: (a) identify phishing lures content on spoofed website, (b) resolve data imbalance via SMOTE-Tomek, and (c) select predictors concerning the target class via the relief rank feature selection.

Resolving data imbalance via oversampling has become imperative in ML, as it accounts for the minor class as crucial [67][68]. It is opposed to under-samplers that often reduces or ignore as meaningless, the minor class in a dataset [69]. Thus, we use the synthetic minority oversample technique (SMOTE) [70], or its variants namely SMOTE-Tomek [71] and SMOTEEN [22]. Our study contributes thus: Section 1 introduces the subject with gaps that motivate the study, (b) Section 2 explores the proposed method – and leans on data collection, pre-processing, dataset split-balance-normalize via SMOTE-Tomek, the stacked model construction, training and validation with XGBoost, and (c) Section 3 – discusses the experimental results obtained as evidence in a broader sense cum context for the stacked ensemble on the phishing website dataset as obtained from UCI.

1. Scam count by age distribution

2. Scam count by gender

3. Students’ status by year of study

2. Materials and Methods

The stacking mode is based on Fig. 4 as thus:

• Step-1–Data Collection: We explore UCI phishing dataset, that consist of 11,055-records distributed into 5,180-cases in genuine class, and 5,875-cases in phishing class [72][73]. The original dataset plot is seen in Fig. 5 and detailed in Table 1.
• Step-2–Pre-processing: cleans up the dataset by expunging redundancies to yield integrity, and removes missing values to yield quality. The optimized data is encoded via a one-hot encoding that transforms categorical data into its equivalent binary forms [74]. Fig. 6 shows the optimized dataset.
• Step-3–Relief Rank Feature Selection: We select strictly, only the predictors of relevance to expunge all docile feats and reduce dataset dimensionality, to aid fastened model construction [75]. The relief rank: (a) assumes all features have same weight and influence on accuracy, (b) identifies the nearest sample from the same class as the nearest hit, and the nearest sample from a varying class as the nearest miss, and (c) uses feature value of nearest neighbor to update its weight(s) [76]. It assesses the correlation of all predictors for a target class as in (1). With a threshold of 8.321 computed, Algorithm 1 ranks features using relief ranking to choose a total of 20 predictors as in Table 1, from the original UCI dataset with the initial 30 features.

• Step-4–Data Split/Balance: First, dataset is split into train (75%) and test (25%). Balancing resample data, interpolating its nearest neighbor to create synthetic data to repopulate a pool, or removing data from its original pool to yield a more balanced dataset. We adapt SMOTE-Tomek via the SMOTE (oversampler) and Tomek-links (under-sampler) as detailed in [77][78]. Fig. 7 shows a balanced plot as resulting from the Algorithm 2 for SMOTE-Tomeks.

4. Proposed stacking ensemble approach with XGBoost as
   meta-regressor

1. Ranking of Features Engineered Using Wrapper Mode

5. Original dataset plot

6. Preprocessing applied to the dataset

7. SMOTE-Tomek data balancing

• Step-5 – Stacked-Ensemble fuses 3-base learners with the XGB meta-regressor, explained as:

1. Cultural Genetic Algorithm uses these belief spaces as: (a) normative values to which predictors are bound, (b) domain equip predictors with knowledge about task, (c) temporal ensures each predictor knows the solution, and (d) spatial ensures each [79] predictor with its topology. It uses an influence function to set its (upper and lower) bounds which lies between (0,1) in its quest for optimal as in (2); and (3) – allows knowledge transfer between its belief space(s) and the pool, and to alter each predictor to conform with its belief space [80]. Each bi {1,0} is a chromosome gene [81]. Table 2 is the CGA design.

2. CGA Design and Configuration

2. Random Forest successively grows its decision trees independently via a bootstrap sample, in bagging mode [82]. It uses a binary split on its extra layer to extend the randomness on how its trees are constructed, so that its best nodes are selected randomly to capture intricate feats in the dataset [83]. Its inability to handle diversity in categorical data often results in its poor performance [84]. Thus, we tune the hyperparameters to reduce model overfitting [85]. Expressed in (4), with  as normalized feature importance for  in tree j in (5). T is the total number of trees, and  is the importance of a feature  about ground-truth, and  is nodal feature importance as in (6) that yields Gini value [86]. Table 3 shows the Random Forest design configuration.

3. Rf Design Configuration

3. Korhonen Modular Neural Network (KMNN) yields a deep, modular learning model that computes its output using the tan-sigmoid function. It splits a network into smaller units for enhanced dependence and improved efficacy of its units [87]. This improves its computational efficiency, reduces time convergence, and lets it handle more tasks effectively in parallel [88]. Its diversity grants each unit independent training to make KMNN more robust and flexible, with improved generalization [89][90]. Table 4 details the KMNN design configuration.

4. Korhonen Modular NN Configuration

4. XGBoost meta-regressor leans on the predictive output of its base models, expanding its goal function through its regularizer term Ω() and loss function l() [91] to ensure its solution remains within the bounds for its improved accuracy via its tuned hyperparameters [92] as in Table 5 and (7).

5. XGB Regressor Design and Configuration

• Step 6 – Train/Cross Validation: is initialized with default configuration as in Table 2 to Table 5 to tune hyperparameters. Each tree is iteratively constructed and trained to ensure the collective knowledge is used in identifying intricate patterns. Training blends synthetic with original data that guarantees comprehensive learning, while improving its adaptability for a variety of configurations [93][94].

3. Result Findings and Discussion

1. Results, Findings, and Discussion

For a comprehensive evaluation devoid of overfitting, we use a 5-fold cross-validation on the 70% train-subset obtained via SMOTE-Tomek balancing, and a final evaluation carried out via a held-out test (30%) dataset as in Table 6. Proposed hybrid yields an average accuracy of 99.34% with a Precision of 99.6%, a Recall of 98.64%, an F1 of 99.2%, a Specificity of 99.66%, an MCC of 97.7% and an AUC-ROC of 99.6%.

From Table 6, the high value resulting in the MCC scores implies that the model accurately and consistently handles the minority class with data balancing performed; while the Specificity value of 99.66% reached indicates that the model effectively recognizes phishing, malicious websites that agree with [95]. The held-out test (30%) assesses the model’s generalization ability with unseen data. The results showed an accuracy of 99.7%, precision 100%, recall of 99.8%, and F1 of 99.5%. The AUC value of 99.7% implies that the model was able to differentiate between the benign and malignant records. Also, a Specificity of 100% indicates that no benign (phishing) record was misclassified.

6. Evaluation Without Feature Selection

Fig. 8 is the AUC-ROC with a 99.73%, and shows the model’s capability to differentiate the negative and positive classes. The proposed model accurately identified all 3,591 of the test data. With only a misclassified case and no false positives recorded [96] – Its specificity of 1.000 implies that no phishing content was misclassified. This is critical to avoid misclassification when detecting phishing. Proposed model enhances phishing website detection performance on both the training data and the held-out test set [97].

Fig. 9 implies the ensemble correctly classified all test datasets with perfect accuracy. The utilization of both feature selection, SMOTE-Tomek balancing, and data normalization did not degrade performance [98][99]. Rather, it focuses on critical feats for model construction to successfully detect spoofed websites with reduced errors that will secure user(s) resources and enhance experience.

8. ROC result of the held-out test dataset

9. Confusion Matrix

1. Comparison

As we explore the high performance of our proposed ensemble with the dataset to demonstrate its robust flexibility, adaptability, robustness, and prediction ability, we also benchmark it against previous methods that have used the same dataset  [100]. Thus, we benchmark our ensemble's similar design constructs on various datasets for various domain tasks, as in Table 7 [101]. We focus on the held-out test dataset performance as it presents a more realistic indication of the model’s generalization capabilities. These are summarized using the metric Table 7:

7. Evaluation Without Feature Selection

The proposed model underperforms against [103] due to its use of BiGRU deep learning scheme with hybrid feature selection; However, other benchmark model underperformed in comparison to our proposed model, across metrics on the test dataset – achieving its high accuracy (99.7%), precision (100%), recall (99.8%) and specificity (100%) – showing best generalization with low false-positives, which is crucial in phishing detection especially with complex lures used by adversaries [105] in their evolving exploit methods. Models leverage deep learning capabilities – their performance can be seen to be slightly lower in metrics, and the lack thereof of specificity indicates that they are less robust; whereas, our model can be seen to maintain high sensitivity performance, even with its transfer learning architectures. We used the SMOTE-Tomek scheme to address class imbalances [57].

4. Conclusion

This study presents a hybrid fusion of supervised CGA with unsupervised KMNN, and tree-based (RF and XGB) to classify websites via the UCI phishing website dataset. The model achieved high-performing discriminative ability by fusing statistically selected features using the relief ranking mode. It used SMOTE-Tomek at training successfully to mitigate imbalance in classes to yield enhanced recall and F1. Its final classification with the XGB kernel achieved a 99.7% accuracy with 100% precision on test data. The comparative analysis with benchmarks showed our method’s superior generalization and data balance. Thus, our study contributes a light framework yet effective model that avoids complex training, handles larger dataset complexities, and proffers interpretability with high performance. Future work may extend this hybrid strategy to multiclass or multimodal datasets and test alternative fusion or dimensionality reduction.

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