On Determining the Most Effective Subset of Features for Detecting Phishing Websites

Hassan, Doaa

doi:10.5120/21813-5191

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The breakdown of these features is as follows, the total number of features in each section has been added in the parentheses: Body (76), Header (68), URL (24). At the next level, the totals are: Semantic (30), Syntactic (79), and Pragmatic (34). One exemplar feature from each class is given below.…”

Section: Email Featuresmentioning

confidence: 99%

An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

et al. 2020

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We perform an in-depth, systematic benchmarking study and evaluation of phishing features on diverse and extensive datasets. We propose a new taxonomy of features based on the interpretation and purpose of each feature. Next, we propose a benchmarking framework called 'PhishBench,' which enables us to evaluate and compare the existing features for phishing detection systematically and thoroughly under identical experimental conditions, i.e., unified system specification, datasets, classifiers, and evaluation metrics. PhishBench is a first in the field of benchmarking phishing related research and incorporates thorough and systematic evaluation and feature comparison. We use PhishBench to test methods published in the phishing literature on new and diverse datasets to check their robustness and scalability. We study how dataset characteristics, e.g., varying legitimate to phishing ratios and increasing the size of imbalanced datasets, affect classification performance. Our results show that the imbalanced nature of phishing attacks affects the detection systems' performance and researchers should take this into account when proposing a new method. We also found that retraining alone is not enough to defeat new attacks. New features and techniques are required to stop attackers from fooling detection systems. INDEX TERMS Feature engineering, feature taxonomy, framework, phishing email, phishing URL, phishing website.

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Section: Email Featuresmentioning

confidence: 99%

An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

et al. 2020

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“…Random Forest (RF) has also been successful in distinguishing phishing attacks from normal websites, with Subasi et al [40] achieving an exceptional classification performance of 97.36% using the random forest classifier. Feature selection has been a focus in another study, with characteristics grouped to identify the most effective ones for accurate phishing attack detection [41].…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Phishing Website Detection

Omari

2023

IJACSA

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Phishing, a prevalent online threat where attackers impersonate legitimate organizations to obtain sensitive information from victims, poses a significant cybersecurity challenge. Recent advancements in phishing detection, particularly machine learning-based methods, have shown promising results in countering these malicious attacks. In this study, we developed and compared seven machine learning models, namely Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Gradient Boosting, to assess their efficiency in detecting phishing domains. Employing the UCI phishing domains dataset as a benchmark, we rigorously evaluated the performance of these models. Our findings indicate that the Gradient Boosting-based model, in conjunction with the Random Forest, exhibits superior performance compared to the other techniques and aligns with existing solutions in the literature. Consequently, it emerges as the most accurate and effective approach for detecting phishing domains.

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“…In any Machine Learning based model, 'Feature Selection [106], [107] and Feature Engineering' is a really crucial task as it is used to derive new and novel features from the existing ones to better facilitate the subsequent learning and generalization steps if a Machine Learning based algorithm is deployed to build a model [108]. The performance of the built model can often drastically improve if an intelligent and intuitive Feature Selection and Engineering phase can be executed beforehand.…”

Section: ) Feature Selection and Engineeringmentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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On Determining the Most Effective Subset of Features for Detecting Phishing Websites

Cited by 8 publications

References 18 publications

An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

Comparative Study of Machine Learning Algorithms for Phishing Website Detection

A Comprehensive Survey for Intelligent Spam Email Detection

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