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Journal of Network and Systems Management

Erschienen in:

01.07.2021

URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models

verfasst von: Sara Afzal, Muhammad Asim, Abdul Rehman Javed, Mirza Omer Beg, Thar Baker

Erschienen in: Journal of Network and Systems Management | Ausgabe 3/2021

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Abstract

Malicious Uniform Resource Locators (URLs) embedded in emails or Twitter posts have been used as weapons for luring susceptible Internet users into executing malicious content leading to compromised systems, scams, and a multitude of cyber-attacks. These attacks can potentially might cause damages ranging from fraud to massive data breaches resulting in huge financial losses. This paper proposes a hybrid deep-learning approach named URLdeepDetect for time-of-click URL analysis and classification to detect malicious URLs. URLdeepDetect analyzes semantic and lexical features of a URL by applying various techniques, including semantic vector models and URL encryption to determine a given URL as either malicious or benign. URLdeepDetect uses supervised and unsupervised mechanisms in the form of LSTM (Long Short-Term Memory) and k-means clustering for URL classification. URLdeepDetect achieves accuracy of 98.3% and 99.7% with LSTM and k-means clustering, respectively.

Vorheriger Artikel Customizable Deployment of NFV Services

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Titel: URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models
verfasst von: Sara Afzal
Muhammad Asim
Abdul Rehman Javed
Mirza Omer Beg
Thar Baker
Publikationsdatum: 01.07.2021
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 3/2021
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-021-09587-8

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