Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation

Sindhu, S.; Patil, Sunil; Sreevalsan, Arya; Rahman, Faiz Ur; Saritha, A

doi:10.1109/icstcee49637.2020.9277256

Cited by 27 publications

(6 citation statements)

References 12 publications

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“…When we compare our results with Sindhu et al [24] from TABLE VII, we find that our novel proposed models SMOTEENN-XGB (Accuracy 99.82%, F1 0.9982 and SMOTEENN-RF (Accuracy 99.47%, F1 0.9947), performed significantly better than their RF (Accuracy 97.37%), SVM (Accuracy 97.451%), and NN with Back Propagation (Accuracy 97.260%). Aminu et al [25] employed Random Forest Feature Importance algorithm to select top 24 features from the same dataset [10] as used by us in this study.…”

Section: ) Comparison and Validation Of Our Proposed Approach With Th...mentioning

confidence: 57%

“…Bikku et al [23] proposed an Optimized Random Forest Algorithm by hyper tuning of the Random Forest classifier. Sindhu et al [24] employed lexical feature analysis on the dataset [10] and developed three ML models using RF, SVM and Neural Network with Backpropagation, and implemented their best SVM (Accuracy 97.451%) model as the Google Chrome browser extension. Aminu et al [25] selected top 24 features of dataset [10] using RF Feature Importance algorithm, followed by XGBoost for classification.…”

Section: ) Related Workmentioning

confidence: 99%

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SMOTEEN Hybrid Sampling Based Improved Phishing Website Detection

Srivastava¹,

Sharan²

2022

Preprint

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Section: ) Comparison and Validation Of Our Proposed Approach With Th...mentioning

confidence: 57%

Section: ) Related Workmentioning

confidence: 99%

SMOTEEN Hybrid Sampling Based Improved Phishing Website Detection

Srivastava¹,

Sharan²

2022

Preprint

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“…The RF algorithm is one of a supervised learning method that includes a series of tree predictors, where each tree is based on the values of a randomly sampled vector with the same distribution across all trees in the forest. Thus, the results of each of these trees are calculated separately and then combined to provide a favorable prediction [ 54 , 55 ]. RF is an enhanced version of the decision tree algorithm, considering that the classification capacity of a single tree may be small.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Astolfi

Silva

Guedes

et al. 2023

Sensors

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Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

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“…Sindhu et al [108] applied the current machine learning algorithms used to identify phishing websites. Improved Random Forest Classification System, SVM, and Network Classification Methods for backpacked reproduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Web Phishing Detection Using Web Crawling, Cloud Infrastructure and Deep Learning Framework

Abdulrahman¹,

Ahmed²,

Rashid³

et al. 2023

JASTT

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The pandemic of COVID-19 obliges citizens to follow the “work from home “scheme. The Internet is also a powerful channel for social connections. The huge dependency of people on digital media opens doors to fraud. Phishing is a form of cybercrime that is used to rob users of passwords from online banking, e-commerce, online schools, digital markets, and others. Phishers create bogus websites like the original and deliver users spam mails. When an online user visits fake web pages via spam, phishers steal their credentials. As a result, it is important to identify these forms of fraudulent websites until they do any harm to victims. Inspired by the ever-changing existence of phishing websites. This paper reviews the work on Phishing attack detection and aims to examine techniques that mainly detect and help in preventing phishing attacks rather than mitigating them. Here we offered a general overview of the most effective phishing attack detection strategies focused on deep learning.

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Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation

Cited by 27 publications

References 12 publications

SMOTEEN Hybrid Sampling Based Improved Phishing Website Detection

SMOTEEN Hybrid Sampling Based Improved Phishing Website Detection

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Web Phishing Detection Using Web Crawling, Cloud Infrastructure and Deep Learning Framework

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