AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

Alsariera, Yazan A.; Adeyemo, Victor Elijah; Balogun, Abdullateef Oluwagbemiga; Alazzawi, Ammar K.

doi:10.1109/access.2020.3013699

Cited by 86 publications

(36 citation statements)

References 27 publications

(45 reference statements)

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“…The data-set contained 32 pre-processed features with 11,055 websites. Authors in Alsariera et al [ 13 ] used four meta-student models: AdaBoost-Extra Tree (ABET), Bagging-Extra tree (BET), Rotation Forest-Extra Tree (RoFBET), and LogitBoost-Extra Tree (LBET), using the extra-tree base classifier. The proposed meta-algorithms were fitted for phishing website datasets, and their performance was tested.…”

Section: Literature Surveymentioning

confidence: 99%

A comprehensive survey of AI-enabled phishing attacks detection techniques

et al. 2020

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In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

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Section: Literature Surveymentioning

confidence: 99%

A comprehensive survey of AI-enabled phishing attacks detection techniques

et al. 2020

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“…The detection models are developed based on the 10-fold cross-validation (CV) technique. The preference for a 10-fold CV is based on its ability to produce models with low bias and variance [43][44][45][46]. Also, spam detection models with or without dimensionality reduction were developed to have an unprejudiced comparison and to evaluate the effect of dimensionality reduction and ensemble methods in spam detection.…”

Section: Model Construction Phasementioning

confidence: 99%

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Oladepo

Bajeh

Balogun

et al. 2021

Int. J. Interact. Mob. Technol.

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This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

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“…In [5] Yazan A et al, proposed AI meta learners combined with base algorithm known as "Extra Tree Classifier". The first meta learner is "ABET", this process is carried for 100 iterations and later normal distribution is performed.…”

Section: Related Workmentioning

confidence: 99%

Feature Engineering Framework to detect Phishing Websites using URL Analysis

Goud¹,

Mathur²

2021

IJACSA

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Phishing is a most popular and dangerous cyberattack in the world of internet. One of the most common attacks in cyber security is to access the personal information of internet users through "Phishing Website". The major element through which hacker can do this job is through URL. Hacker creates an almost replica of original URL in which there is a very small difference, generally not revealed without keen observation. By pipelining various machine learning algorithms, the proposed model aims to recognize the important features to classify the URL using a recursive feature elimination process. In this work the data set of various URL records has been collected with 112 features including one target value. In this work a Machine Learning based model is proposed to identify the significant features, used to classify a URL, the wrapper method recursive feature elimination compares different bagging and boosting machine learning approaches .Ensemble algorithms, Bootstrap Aggregation Algorithms, Boosting and stacking algorithms are used for feature selection. The proposed work has five sections: work on the pre-processing phase, finding the relation between the features of the dataset, automatic selection of number of features using Extra Tree Classifier, comparison of the various ensemble algorithm and finally generates the best features for URL analysis. This paper, designs meta learner with XG BOOST classifier as base classifier and achieved an accuracy of 93% Out of 112 features, this model has performed an extensive comparative study on feature selection and identified 29 features as core features by performing URL analysis.

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AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

Cited by 86 publications

References 27 publications

A comprehensive survey of AI-enabled phishing attacks detection techniques

A comprehensive survey of AI-enabled phishing attacks detection techniques

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Feature Engineering Framework to detect Phishing Websites using URL Analysis

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