A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites

Kalabarige, Lakshmana Rao; Pais, Alwyn Roshan; Gabralla, Lubna Abdelkareim

doi:10.1109/access.2023.3293649

Cited by 9 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approaches based on feature importance have proven to be more accurate by evaluating and ranking each feature according to its contribution to the model. However, these methods, particularly in the context of ensemble models based on deep learning, have not been thoroughly investigated [29].…”

Section: Introductionmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

View full text Add to dashboard Cite

Phishing attacks pose a significant and escalating threat to cybersecurity in recent times. This deceptive scam aims to trick naive users, luring them into visiting harmful websites and sharing sensitive information, including credentials, credit card numbers, and passwords. Consequently, cybercriminals exploit this data for their own gain. As the sophistication and maliciousness of phishing continue to evolve, researchers are earnestly developing multiple anti-phishing solutions in the literature. Among these solutions, those based on deep learning models have gained substantial attention in recent years. This study proposes an intelligent, deep-learning-based mechanism to detect phishing URLs. The proposed system is based on the permutation importance method (PIM) to select the most relevant URL features, and the Smote-Tomek link method to solve the problem of an unbalanced dataset. In addition, four DL models—CNN, LSTM, and two hybrid models (CNN-LSTM and LSTM-CNN)—are tested to identify the more suitable detection model for the phishing field. The experimental results demonstrate the successful functioning of the proposed phishing detection mechanism. It is observed that the proposed mechanism achieved an accuracy ranging from 93.36% to 96.43% without feature selection and data balance across two variants of datasets and different DL classifiers. It also achieved an accuracy ranging from 94.12% to 96.88% with feature selection and data balance. Finally, our phishing detection mechanism is implemented as web application to enhance its usability for web users.

show abstract

Section: Introductionmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

View full text Add to dashboard Cite

show abstract

Exploring Multi-attribute Selection Strategies for Effective Phishing Detection with Machine Learning

Arundhati

2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection

Kavya,

Sumathi

2024

Artif Intell Rev

View full text Add to dashboard Cite

The escalating threat of phishing attacks poses significant challenges to cybersecurity, necessitating innovative approaches for detection and mitigation. This paper addresses this need by presenting a comprehensive review of state-of-the-art methodologies for phishing detection, spanning traditional machine learning techniques to cutting-edge deep learning frameworks. The review encompasses a diverse range of methods, including list-based approaches, machine learning algorithms, graph-based analysis, deep learning models, network embedding techniques, and generative adversarial networks (GANs). Each method is meticulously scrutinized, highlighting its rationale, advantages, and empirical results. For instance, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), demonstrate superior detection performance, leveraging their ability to extract complex patterns from phishing data. Ensemble learning techniques and GANs offer additional benefits by enhancing detection accuracy and resilience against adversarial attacks. The impact of this review extends beyond academic discourse, informing practitioners and policymakers about the evolving landscape of phishing detection. By elucidating the strengths and limitations of existing methods, this paper guides the development of more robust and effective cybersecurity solutions. Moreover, the insights gleaned from this review lay the groundwork for future research endeavors, such as integrating contextual information, user behavior analysis, and explainable AI techniques into phishing detection systems. Ultimately, this work contributes to the collective effort to fortify digital defenses against sophisticated phishing threats, safeguarding the integrity of online ecosystems.

show abstract

A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites

Cited by 9 publications

References 45 publications

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Exploring Multi-attribute Selection Strategies for Effective Phishing Detection with Machine Learning

Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection

Contact Info

Product

Resources

About