An Intelligent Phishing Detection Scheme Using Machine Learning

Makkar, Aaisha; Kumar, Neeraj; Sama, Lakshit; Mishra, Satyam; Samdani, Yash

doi:10.1007/978-981-15-8061-1_13

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…The machine learning approach formulated by Makkar et al [13] evaluated bagged AdaBoost, bayesian generalized linear model, Naïve Bayes, linear SVM with class weights, ensembles of generalized linear models, monotone multilayer perceptron neural network, quadratic discriminant analysis, multilayer perceptron, neural networks with feature extraction, and oblique RF. Following that, 10 rounds of cross-validation are performed on an ensemble of the best three best in terms of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Phishing Website Detection Using Classification Algorithms

Raj

Jothi

2022

paradigmplus

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The internet has significantly altered how we work and interact with one another.Statistics show 63.1 % of the present world population are internet users. This clearly indicates how heavily man is dependent on digital media. Digital media users are on the rise and so is the incidence of cyber crimes. People who lack experience and knowledge are more vulnerable and susceptible to phishing scams.The victims experience severe consequences as their personal credentials are at stake. Phishers use publicly available sources to acquire details about the victim's professional and personal history.Countermeasures must be implemented with the highest priority. Detection of malicious websites can significantly reduce the risk of phishing attempts.In this research, a highly accurate website phishing detection method based on URL features is proposed. We investigated eight existing machine learning classification techniques for this, including extreme gradient boosting (XGBoost), random forest (RF), adaptive boosting (AdaBoost), decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression and naïve bayes (NB) to detect malicious websites.The results show that XGboost had the best accuracy with a score of 96.71%, followed by random forest and AdaBoost.We further experimented with various hybrid combinations of the top three classifiers and observed that XGboost-Random Forest hybrid algorithms produced the best results.The hybrid model classified the websites as legitimate or phishing with an accuracy of 97.07%.

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Section: Related Workmentioning

confidence: 99%