Stop-Phish: an intelligent phishing detection method using feature selection ensemble

Ramana, Attili Venkata; Rao, K. Lakshmana; Rao, Routhu Srinivasa

doi:10.1007/s13278-021-00829-w

Cited by 17 publications

(3 citation statements)

References 26 publications

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“…For example, filter and wrapper methods are applied in [112] to select -among 177 features derived from the web page URL and source code -the optimal subset of features. A feature selection ensemble is proposed in [116]. According to this ensemble, multiple sets of features are generated by different selection methods, thus promoting diversity and improving generalization.…”

Section: B Feature Selectionmentioning

confidence: 99%

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

2023

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Phishing is a security threat with serious effects on individuals as well as on the targeted brands. Although this threat has been around for quite a long time, it is still very active and successful. In fact, the tactics used by attackers have been evolving continuously in the years to make the attacks more convincing and effective. In this context, phishing detection is of primary importance. The literature offers many diverse solutions that cope with this issue and in particular with the detection of phishing websites. This paper provides a broad and comprehensive review of the state of the art in this field by discussing the main challenges and findings. More specifically, the discussion is centered around three important categories of detection approaches, namely, list-based, similarity-based and machine learning-based. For each category we describe the detection methods proposed in the literature together with the datasets considered for their assessment and we discuss some research gaps that need to be filled.

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Section: B Feature Selectionmentioning

confidence: 99%

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

2023

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“…In a similar study, Ramana et al [6] developed an ensemble of XGBoost, RF, and DT based on feature selection to classify phishing websites. Experiments were conducted using UCI and Mendeley datasets.…”

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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“…Ramana et al [18] presented a smart technology using an ensemble of feature selection techniques to detect the phishing sites and achieve considerable results. The study employed different ML methods to find out the optimal classification method and proposed an ensemble technique using Extreme Gradient Boosting (XGBoost), Random forest, (RF), and Decision tree (DT) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

Shaiba¹,

Eltahir²,

Marzouk³

et al. 2022

Computers, Materials &Amp; Continua

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Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses. The tremendous growth and evolution of Deep Learning (DL) techniques paved the way for increasing the accuracy of classification process. In this background, the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification (HSOHDL-PDC) model. The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs. In addition, SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance. Moreover, a hybrid Convolutional Neural Network-Long Short Term Memory (HCNN-LSTM) technique is also applied for identification and classification of phishing. The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes. The performance of the proposed HSOHDL-PDC model was validated using 6426 CMC, 2022, vol.73, no.3 different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

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Stop-Phish: an intelligent phishing detection method using feature selection ensemble

Cited by 17 publications

References 26 publications

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

Hybrid Approach for Phishing Website Detection Using Classification Algorithms

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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