A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

Chiew, Kang Leng; Tan, Choon Lin; Wong, KokSheik; Yong, Kelvin S. C.; Tiong, Wei King

doi:10.1016/j.ins.2019.01.064

Cited by 252 publications

(157 citation statements)

References 24 publications

(45 reference statements)

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“…More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem. Many authors have conducted experiments using various classification methods and different phishing datasets with predefined features (Chiew et al, 2019;Marchal et al, 2016;Sahoo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art methods of phishing website detection report classification accuracy (the classification accuracy measure is described in Section 3.4.1) well above 99.50% and use different classification algorithms: ensembles (Gradient Boosting) (Marchal et al, 2017), statistical models (Logistic Regression) (Whittaker et al, 2010), probabilistic algorithms (Bayesian Network) (Xiang et al, 2011), classification trees (C4.5) (Cui et al, 2018). There is no common agreement about what classification algorithm is the most accurate in phishing website prediction on datasets with predefined features (Chiew et al, 2019). 2.…”

Section: Introductionmentioning

confidence: 99%

“…Classification accuracy measure has low construct validity on datasets where class balance is not proportional and show better results for the preferred class. Doubts remain, whether these results were demonstrated due to dataset dependent method design (Chiew et al, 2019) or algorithms used in state-of-the-art research are preeminent compared to others. 3.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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“…Hybrid Ensemble Feature Selection (HEFS) method [12] was proposed for phishing detection. Initially, primary feature subsets were generated by using a Cumulative Distribution Function gradient (CDF-g) algorithm and those features were acted as input to the data perturbation ensemble method.…”

Section: Literature Surveymentioning

confidence: 99%

“…A Case-Based Reasoning Phishing Detection System (CBR-PDS) [13] was introduced to detect the phishing websites. It primarily based on the CBR which act as animportant part of phishing detection system.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection

Kothandan¹,

Sujatha²

2019

IJMLNCE

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ensitive information such as credit card information, username, password and social security number etc, can be stolen using a fake page that imitates trusted website is called phishing. The attacker designs a similar webpage either by copying or making small manipulation to the legitimate page so that the online user cannot distinguish the legitimate and fake websites. A Deep Neural Network (DNN) was introduced to detect the phishing Uniform Resource Locator (URL). Initially, a 30-dimension feature vector was constructed based on URLbased features, Hypertext Markup Language (HTML)-based features and domainbased features. These features were processed in DNN to detect the phishing URL. However, the irrelevant, redundant and noisy features in the dataset increase the complexity of DNN classifier. So the feature selection is required for efficient phishing attack detection. But feature selection is a time-consuming process since it is an independent process. So in this paper, a feature vector is generated by DNN itself using Stacked Denoise Auto Encoder (SDAE). Moreover, the noisy data such as missing features affect the efficiency of phishing detection so the SDAE is trained to reconstruct a clean input feature vector. The initial input feature vector is corrupted by setting some feature vectors as zero. Then, the corrupted feature vector is then plotted with basic auto encoder, to a hidden representation from that the input feature vector is reconstructed. The reconstructed features are given as input to DNN which selects the most relevant features and predicts the phishing URL. Hence the sparse feature representation of SDAE increases the classification accuracy of DNN. The experiments are conducted in Ham, Phishing Corpus and Phishload datasets to prove the effectiveness of DNN-SDAE.

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Evolutionary ensemble feature selection learning for image‐based assessment of lymphedema arm volume

Notash

Bayat

Haghighat

et al. 2021

Concurrency and Computation

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Summary Arm lymphedema is a side effect of surgery and radiation therapy in patients with breast cancer. There are several methods to measure lymphedema arm volume, such as water displacement (WD) and circumferential measurement (CM). Although these techniques have a satisfactory level of accuracy, they have some limitations for clinical use in practical applications. To overcome these drawbacks, a horizontal‐vertical image scanning (HVIS) tool, is presented as a valid method for arm volume measurement. To predict the arm volume, an evolutionary ensemble feature selection learning (named EEFSL) comprising different base learners (KNN, SVM, ANFIS, Decision Tree, and Naïve Bayes) is presented. In order to improve the performance of the EEFSL model, its hyperparameters are optimized using genetic algorithm (GA). The hyperparameters include the weights of different base learners and the specific features for each base learner. The fitness function of GA is to maximize the correlation between predicted and actual arm volumes. The proposed method was successfully developed for arm measurement of 60 arms from 30 women with arm lymphedema. Obtained results indicate that the proposed HVIS‐EEFSL method is a reliable and valid technique to measure arm volume of patients with lymphedema, suitable for daily clinical use instead of WD and CM.

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A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

Cited by 252 publications

References 24 publications

Comparison of Classification Algorithms for Detection of Phishing Websites

Comparison of Classification Algorithms for Detection of Phishing Websites

Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection

Evolutionary ensemble feature selection learning for image‐based assessment of lymphedema arm volume

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