Suspicious URLs Filtering Using Optimal RT-PFL: A Novel Feature Selection Based Web URL Detection

Rajitha, Kotoju; Vijayalakshmi, D.

doi:10.1007/978-981-10-5547-8_24

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…In which the URL logs of users accessing a domain name are used as the research object, the performance characteristics of malicious access are mined from multi-dimensions, and the sample markers with inaccurate categories near the critical points in the Gaussian mixed clustering classification results are modified with manual assistance, and then the S4VM algorithm is used to output a detection model that can identify malicious access, and based on the proposed identification model, URL-based malicious access Based on the proposed identification model, a URL-based malicious access identification system is designed and implemented. Most of the past malicious URL detections are based on blacklisting techniques [13][14] , reputation systems [15] , host features [16][17] , honeypot techniques [18] , lexical features and intrusion detection techniques [19] .…”

Section: Current Status Of Domestic and International Researchmentioning

confidence: 99%

Malicious URL Detection Algorithm Based on Multi Neural Network Series

Xiu¹,

Bian²,

Wu³

2021

Preprint

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Convolutional neural network based on attention mechanism and a bidirectional independent recurrent neural network tandem joint algorithm (CATIR) are proposed. In natural language processing related technologies, word vector features are extracted based on URLs, and the extracted URL information features and host information features are merged. The proposed CATIR algorithm uses CNN (Convolutional Neural Network) to obtain the deep local features in the data, uses the Attention mechanism to adjust the weights, and uses IndRNN (Independent Recurrent Neural Network) to obtain the global features in the data. The experimental results shows that the CATIR algorithm has significantly improved the accuracy of malicious URL detection based on traditional algorithms to 96.9%. Keywords: Malicious URL; CATIR; word vector feature; detection

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Section: Current Status Of Domestic and International Researchmentioning

confidence: 99%

Malicious URL Detection Algorithm Based on Multi Neural Network Series

Xiu¹,

Bian²,

Wu³

2021

Preprint

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“…A growing number of cyber-thieves are taking advantage of these same technologies to deceive us and steal our personal information, which is unfortunate for us. The authors of this paper [6] propose a strategy termed optimal RT-PFL for distinguishing harmful URLs identified on websites from non-malicious URLs, which they describe as follows: In order to generate feature components, the data set should be encoded as both lexical and host functions for the URL in order to construct feature components. The function extraction method is responsible for extracting certain characteristics.…”

Section: Related Workmentioning

confidence: 99%

Review on Phishing Attack Detection using Recurrent Neural Network

Gupta¹

2021

IJRASET

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Phishing is a crime that involves the theft of personal information from users. Individuals, corporations, cloud storage, and government websites are all targets for the phishing websites. Anti-phishing technologies based on hardware are commonly utilised, while software-based options are preferred due to cost and operational considerations. Current phishing detection systems have no solution for problems like zero-day phishing assaults. To address these issues, a three-phase attack detection system called the Phishing Attack Detector based on Web Crawler was suggested, which uses a recurrent neural network to precisely detect phishing incidents. Based on the classification of phishing and non-phishing pages, it covers the input features Web traffic, web content, and Uniform Resource Locator (URL). Keywords: Attack detection, Recurrent Neural Network, Deep Learning.

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Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network

Wang

Tian

et al. 2019

Appl Intell

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Suspicious URLs Filtering Using Optimal RT-PFL: A Novel Feature Selection Based Web URL Detection

Cited by 8 publications

References 6 publications

Malicious URL Detection Algorithm Based on Multi Neural Network Series

Malicious URL Detection Algorithm Based on Multi Neural Network Series

Review on Phishing Attack Detection using Recurrent Neural Network

Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network

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