Detection of Phishing Attacks using Visual Similarity Model

Paturi, Radhika; Swathi, L.; Pavithra, K.Sai; Mounika, R.; Alekhya, Ch.

doi:10.1109/icaaic53929.2022.9793231

Cited by 6 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is also some work using a mixture of features [35][36][37][38]. These approaches may require more computational resources and be more complex to implement compared to URL-based methods.…”

Section: Methods Leveraging Multi-view Informationmentioning

confidence: 99%

A Lightweight Multi-View Learning Approach for Phishing Attack Detection Using Transformer with Mixture of Experts

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Phishing poses a significant threat to the financial and privacy security of internet users and often serves as the starting point for cyberattacks. Many machine-learning-based methods for detecting phishing websites rely on URL analysis, offering simplicity and efficiency. However, these approaches are not always effective due to the following reasons: (1) highly concealed phishing websites may employ tactics such as masquerading URL addresses to deceive machine learning models, and (2) phishing attackers frequently change their phishing website URLs to evade detection. In this study, we propose a robust, multi-view Transformer model with an expert-mixture mechanism for accurate phishing website detection utilizing website URLs, attributes, content, and behavioral information. Specifically, we first adapted a pretrained language model for URL representation learning by applying adversarial post-training learning in order to extract semantic information from URLs. Next, we captured the attribute, content, and behavioral features of the websites and encoded them as vectors, which, alongside the URL embeddings, constitute the website’s multi-view information. Subsequently, we introduced a mixture-of-experts mechanism into the Transformer network to learn knowledge from different views and adaptively fuse information from various views. The proposed method outperforms state-of-the-art approaches in evaluations of real phishing websites, demonstrating greater performance with less label dependency. Furthermore, we show the superior robustness and enhanced adaptability of the proposed method to unseen samples and data drift in more challenging experimental settings.

show abstract

Section: Methods Leveraging Multi-view Informationmentioning

confidence: 99%

A Lightweight Multi-View Learning Approach for Phishing Attack Detection Using Transformer with Mixture of Experts

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…These attacks are becoming increasingly sophisticated, exploiting vulnerabilities in email, SMS, and voice communications. The education sector witnessed a dramatic surge of 576% in phishing attacks, while the retail and wholesale sector experienced a 67% drop [27,61].…”

Section: Introductionmentioning

confidence: 99%

Phishing Detection Using Machine Learning: A Model Development and Integration

Adeyemi Onih

2024

IJSMR

View full text Add to dashboard Cite

This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

show abstract

“…19 Software methods for detecting phishing attacks are being developed. Software methods for detecting phishing attacks use techniques such as Blacklists, 20 Whitelists, 21 pattern or text matching methods, 22 visual similarity, 23 and machine and deep learning. 24 Deep and machine learning methods are rapidly gaining popularity in the field of cyber security and the detection of phishing attacks.…”

mentioning

confidence: 99%

Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence

Jafari,

Aghaee‐Maybodi

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryPhishing attacks have a remarkable ability to steal user information by using simple techniques. Phishing attacks steal valuable information, such as user names and passwords. The loss caused by phishing attacks is significant, and every year, millions of dollars are lost by internet users and companies through phishing attacks. Deep learning methods such as CNN neural network are one approach to detecting phishing attacks: deep learning methods, optimization for image processing, and efficient techniques used to process URL strings. A convolutional neural network is a widely used deep learning method in image processing. The limitation of CNN learning to images is one of the main challenges of this neural network for detecting phishing attacks. Imbalance in the training dataset and lack of intelligent feature selection are other challenges for CNN training in detecting phishing attacks. This manuscript presents a new approach called CGAN‐IWSO‐ResNet50 to detect phishing attacks. In the first step, the improved version of the conditional GAN is used to balance the URL samples. In the second stage, Hand‐crafted and TF‐IDF methods implement the feature extraction phase. In the feature selection stage, the WOA algorithm is used to improve the WSO algorithm's performance in feature selection. The selected features are implemented on the dataset, and legal and phishing samples are coded as RGB images. In the last step, RGB images are used to teach ResNet50 architecture. Tests in the PhishTank dataset showed that the proposed method's accuracy, sensitivity, and precision index are 99.65%, 99.12%, and 99.46%, respectively. The CGAN‐IWSO‐ResNet50 method is more accurate in detecting phishing attacks than the VGG19, AlexNet, RCNN, DNN + LSTM, and DNN + BiLSTM learning methods.

show abstract

Detection of Phishing Attacks using Visual Similarity Model

Cited by 6 publications

References 13 publications

A Lightweight Multi-View Learning Approach for Phishing Attack Detection Using Transformer with Mixture of Experts

A Lightweight Multi-View Learning Approach for Phishing Attack Detection Using Transformer with Mixture of Experts

Phishing Detection Using Machine Learning: A Model Development and Integration

Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence

Contact Info

Product

Resources

About