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

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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.

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Notes

  1. https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/.

  2. https://towardsdatascience.com/machine-learning-text-processing-1d5a2d638958.

  3. https://towardsdatascience.com/tf-idf-for-document-ranking-from-scratch-in-python-on-real-world-dataset-796d339a4089.

  4. https://www.kaggle.com/siddharthkumar25/malicious-and-benign-urls.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan

    Sara Afzal & Muhammad Asim

  2. Department of Cyber Security, Air University, Islamabad, Pakistan

    Abdul Rehman Javed

  3. National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan

    Mirza Omer Beg

  4. Department of Computer Science, University of Sharjah, Sharjah, 27272, UAE

    Thar Baker

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  1. Sara Afzal
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  2. Muhammad Asim
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  3. Abdul Rehman Javed
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  4. Mirza Omer Beg
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  5. Thar Baker
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Correspondence to Muhammad Asim.

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Afzal, S., Asim, M., Javed, A.R. et al. URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models. J Netw Syst Manage 29, 21 (2021). https://doi.org/10.1007/s10922-021-09587-8

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  • DOI: https://doi.org/10.1007/s10922-021-09587-8

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