AI@ntiPhish — Machine Learning Mechanisms for Cyber-Phishing Attack

Abstract

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X 2 , and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study. key words: anti-phishing, machine learning algorithm, ensemble learning mechanism, cyber attack

Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes

“…And, The performance evaluation uses the Iteration value against the cross-validation technique, which is 100. This follows the recommendations from [19] and [37] to obtain maximum performance results accurately. The experimental setting of the subset scheme is shown in Table I.…”

“…MLP is only exceptional on an unbalanced dataset of phishing websites [18]. C4.5 performs best when a phishing webpage balanced and URL unbalanced datasets are used [19], [20]. Similarly, Naïve Bayes exhibits its best performance on a phishing SMS unbalanced dataset [21].…”

“…Several studies have been carried out on phishing prevention, one based on its identification and classification. Various techniques are used for the classification process, such as Random forest [4]- [10], support vector machine (SVM) [11]- [14], Logistic regression [15]- [17], Multilayer perceptron (MLP) [18], C4.5 [19] & [20], and Naïve Bayes [21]. Each exhibits maximum performance according to the case it was applied.…”

A comprehensive survey of AI-enabled phishing attacks detection techniques

“…They used ML algorithms such as DT, RF, Gradient Boosting (GBM), Generalized Linear Model (GLM), and PCA. The authors in Chen and Chen [ 17 ] used the SMOTE method which improves the detection coverage of the model. They trained machine learning models including bagging, RF, and XGboost.…”

Developing window behavior models for residential buildings using XGBoost algorithm

“…As a new machine learning method, the XGBoost (eXtreme Gradient Boost) method was firstly introduced by Chen [37] in 2016, and has been used in many other applications, such as automotive manufacturing [38], predicting building cooling load [39] and fault detection for HVAC systems [40]. In existing studies, much evidence was available about its advantages (stability, accuracy and efficiency) in modeling complex process over other conventional machine learning methods, such as SVM algorithm [41,42], logistic regression method [43][44][45][46][47][48][49] and KNN/decision tree [50,51]. This study, therefore, was designed to justify its contribution to modeling accuracy of occupant window behavior in buildings, mainly against the most conventional modeling approach, i.e.…”