Phishing URL Detection via CNN and Attention-Based Hierarchical RNN

Huang, Yongjie; Yang, Qiping; Qin, Jinghui; Wen, Weisong

doi:10.1109/trustcom/bigdatase.2019.00024

Cited by 66 publications

(52 citation statements)

References 19 publications

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“…In [35]'s method, through using the Bi-IndRNN model to learn the host features and URL information features, and finally a recall rate of 99.93% is obtained. The proposed model [36] is somewhat similar to our model, and its performance was similar to that of Bi-LSTM. It is observed that our model has some improvements over the previous model, moreover, the results in the table are also encouraging, with an accuracy rate of 99.78%.…”

Section: F Comparison With Methodssupporting

confidence: 56%

Malicious URL Detection Based on a Parallel Neural Joint Model

et al. 2021

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Section: F Comparison With Methodssupporting

confidence: 56%

Malicious URL Detection Based on a Parallel Neural Joint Model

et al. 2021

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“…Da mesma forma, [Huang et al 2019] também ofereceram soluções para phishing, mas, diferente dos autores anteriores, o foco de pesquisa foram páginas web e não e-mail. A proposta dos autores foi um método, denominado PhishingNet, baseado em DL para detecção de URL de phishing, que consiste em um módulo CNN e um módulo RNN baseado em atenção hierárquica.…”

Section: Trabalhos Realizadosunclassified

“…A análise de metadados, ainda que em ameças diferentes,é apresentada nos trabalhos de [Shrestha et al 2015], [Fang et al 2019], [Huang et al 2019] e [Zhang et al 2019], condição que apoia a estratégia de uso dos mesmos algoritmos de DL, no caso, LSTM, MLP, CNN, DBN, SVM e RNN. Além da técnica de autoencoder apresentada por [Liu et al 2019].…”

Section: Framework Para Detecção De Exfiltração Em Hdfs Baseado Em Dlpsunclassified

DLPS baseado em Deep Learning: Nova Abordagem para Detecção de Exfiltração em HDFS

Martins

Weigang

García

et al. 2021

Anais Do X Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2021)

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Este artigo descreve segurança cibernética aplicada a Mídias Sociais com ênfase no uso de HDFS para armazenamento e processamento de grandes volumes de dados. O objetivo foi desenvolver um framework de DLPS baseado em ML que melhore a precisão na identificação de vazamento de dados em estruturas de HDFS. Assim, identificou-se as principais categorias de abordagens em segurança cibernética, no âmbito de HDFS, em comparação com Framework MITRE ATT&CK. Lacunas de pesquisas foram identificadas, em trabalhos realizados envolvendo DLPS e Machine Learning, oferecendo a necessidade do desenvolvimento de soluções correlacionadas. Um framework baseado em Deep Learning aplicado aos metadados e logs do Hadoop é proposto como solução para melhorar a detecção de exfiltração.

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“…In this method, character-level and word level features are extracted based on URL strings and CNN network is used for training and testing. Huang et al [29] proposed the PhishingNet deep learning-based method for detection of phishing URLs. They use a CNN network to extract character-level features of URLs; meanwhile, they employ an attention-based hierarchical recurrent neural network (RNN) to extract word-level features of URLs.…”

Section: Deep Learning-based Detectionmentioning

confidence: 99%

An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

et al. 2020

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Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

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Phishing URL Detection via CNN and Attention-Based Hierarchical RNN

Cited by 66 publications

References 19 publications

Malicious URL Detection Based on a Parallel Neural Joint Model

Malicious URL Detection Based on a Parallel Neural Joint Model

DLPS baseado em Deep Learning: Nova Abordagem para Detecção de Exfiltração em HDFS

An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

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