A deep learning based end-to-end system (F-Gen) for automated email FAQ generation

Jeyaraj, Shiney; Raghuveera, T

doi:10.1016/j.eswa.2021.115896

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Combined word embedded and Neural Bag-of-ngrams enable for extracting syntactic and semantic similarity of emails. DL techniques [14] enable for extracting the abstract and optimum feature representations and fully connected (FC) layer with nonlinear activation function to classifier. According to an enhanced recurrent CNN (RCNN) technique with multilevel vectors and attention process, Fang et al [15] presented a novel phishing email recognition method called THEMIS that is utilized for modeling email at the word level, email header, email body, and character level concurrently.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Dutta¹,

Meyyappan²,

Qureshi³

et al. 2023

Computer Systems Science and Engineering

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Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users' private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing. The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning, tokenization, and stop word elimination. Besides, TF-IDF model is applied for the extraction of useful feature vectors. Moreover, optimal deep belief network (DBN) model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process. The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions. Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

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Section: Related Workmentioning

confidence: 99%