A Character-Level BiGRU-Attention for Phishing Classification

Yuan, Lijuan; Zeng, Zhiyong; Lu, Yikang; Ou, Xiaofeng; Tao, Fengbo

doi:10.1007/978-3-030-41579-2_43

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…The scores of the targets are given to the BiGRU layer. BiGRU [16] is a combination of two GRU (forward and back ward) which is used to find the key features using two gates i) reset gate ignores the irrelevant words and ii) update gate reserves the important information. The gates shall get the value between 0 and 1 where 0 means the resultant data is unimportant and 1 means resultant data is important.…”

Section: Bigrumentioning

confidence: 99%

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Sangeetha¹,

Prabha²

2022

Computer Systems Science and Engineering

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Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students' behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.

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Section: Bigrumentioning

confidence: 99%

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Sangeetha¹,

Prabha²

2022

Computer Systems Science and Engineering

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“…Gated Recurrent Unit (GRU) is another variant of RNN and is a lightweight version of LSTM [23]. While working on small datasets, the performance of GRU is similar to LSTM [48]. Some of the previous studies that implemented GRU in their phishing detection models are provided in Table A4…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

“…There are a limited number of studies on the implementation of GRU for phishing detection. GRU and Bidirectional GRU can be employed as a single classifier [41,48], or as a replacement to the max-pooling layer in a CNN model [34]. Similar to LSTM, in implementing GRU-based phishing detection models, only neural network architecture, learning rate, and epoch were specified, but not batch size and dropout rate [41,48].…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

“…GRU and Bidirectional GRU can be employed as a single classifier [41,48], or as a replacement to the max-pooling layer in a CNN model [34]. Similar to LSTM, in implementing GRU-based phishing detection models, only neural network architecture, learning rate, and epoch were specified, but not batch size and dropout rate [41,48]. Plus, none of the reviewed papers on GRU included parameter optimization in their experiments.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

“…Plus, none of the reviewed papers on GRU included parameter optimization in their experiments. Regarding the performance metrics, all three studies [34,41,48] used accuracy, precision, recall, and F1-score to assess the effectiveness of the DL algorithm. Additional metrics involved GPU memory requirement and parameter set size [34,48].…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

See 2 more Smart Citations

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

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Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.

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An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection

Selamat

Cheng

et al. 2022

Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence

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A Character-Level BiGRU-Attention for Phishing Classification

Cited by 11 publications

References 18 publications

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection

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