An Ensemble Approach for Detecting Phishing Attacks

Agrawal, Himanshi; Singh, Rajni

doi:10.26438/ijcse/v9i7.5359

ijcse

2021

DOI: 10.26438/ijcse/v9i7.5359

|View full text |Cite

An Ensemble Approach for Detecting Phishing Attacks

Himanshi Agrawal¹,

Rajni Singh²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

Ogonji,

Wilson,

Mwangi

2023

ITM Web Conf.

View full text Add to dashboard Cite

Phishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. For prevention, it is essential to develop a phishing detection system. Recent phishing detection systems are based on data mining and machine learning techniques. Most of the related work literature require collection of previous phishing attack logs, analyze them and create a list of such activities and block traffic from such sources. But this is a cumbersome task because the data size is very large, continue changing and dynamic nature. [1]. Instead of using single algorithm approach it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because classification of different format of data is handled; whether the intruder want to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision tress enhances any of machine learning and deep learning algorithms performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positive and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and false negative rate is 7.4%.

show abstract

A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

Ogonji,

Wilson,

Mwangi

2023

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

Hybrid Phishing Detecting with Recommendation Decision Trees

Ogonji,

Wilson,

Mwangi

2024

IJRTE

View full text Add to dashboard Cite

Phishing is performed by trying to trick the victim into accessing any computing information that looks original and then instructing them to send important data to unrestricted/unwanted private resources. For prevention, it is essential to develop a phishing detection system. Recent phishing detection systems are based on data mining and machine learning techniques. Most of the related work literature requires the collection of previous phishing attack logs, analyzing them creating a list of such activities, and blocking traffic from such sources. However, this is a cumbersome task because the data size is very large, continues changing, and is dynamic in nature. [1]. Instead of using a single algorithm approach, it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because the classification of different formats of data is handled; whether the intruder wants to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision trees enhance any of the machine learning and deep learning algorithms' performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positives and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and a false negative rate is 7.4%.

show abstract

An Investigation of AI-Based Ensemble Methods for the Detection of Phishing Attacks

Alsariera,

Alanazi,

Said

et al. 2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Phishing attacks remain a significant cybersecurity threat in the digital landscape, leading to the development of defense mechanisms. This paper presents a thorough examination of Artificial Intelligence (AI)-based ensemble methods for detecting phishing attacks, including websites, emails, and SMS. Through the screening of research articles published between 2019 and 2023, 37 relevant studies were identified and analyzed. Key findings highlight the prevalence of ensemble methods such as AdaBoost, Bagging, and Gradient Boosting in phishing attack detection models. Adaboost emerged as the most used method for website phishing detection, while Stacking and Adaboost were prominent choices for email phishing detection. The majority-voting ensemble method was frequently employed in SMS phishing detection models. The performance evaluation of these ensemble methods involves metrics, such as accuracy, ROC-AUC, and F-score, underscoring their effectiveness in mitigating phishing threats. This study also underscores the availability of credible open-access datasets for the progressive development and benchmarking of phishing attack detection models. The findings of this study suggest the development of new and optimized ensemble methods for phishing attack detection.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Ensemble Approach for Detecting Phishing Attacks

Cited by 3 publications

References 0 publications

A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

Hybrid Phishing Detecting with Recommendation Decision Trees

An Investigation of AI-Based Ensemble Methods for the Detection of Phishing Attacks

Contact Info

Product

Resources

About