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	Natural language based malicious domain detection using machine learning and deep learning </div>

Abdul Samad Saleem Raja, Ganesan Pradeepa, Somasundaram Mahalakshmi, Manickam Sam Jayakumar

2023 , VOLUME 23, NUMBER 2 ( March-April )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-2-304-312

Natural language based malicious domain detection using machine learning and deep learning

A. Saleem Raja, G. Pradeepa, S. Mahalakshmi, M. Sam Jayakumar

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Article in English

For citation:

Saleem Raja A.S., Pradeepa G., Mahalakshmi S., Jayakumar M.S. Natural language based malicious domain detection using machine learning and deep learning. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 304–312. doi: 10.17586/2226-1494-2023-23-2-304-312

Abstract

Cyberattacks are still challenging since they are increasing day by day. Cybercriminals employ a variety of strategies to manipulate and exploit their targets vulnerabilities. Malicious URLs are one such strategy which is used to target large groups on various social media platforms. To draw internet users, these web addresses are disguised as being safe. Deliberate or inadvertent use of such URLs exposes the user or the organization in the cyberspace and opens the way for further attacks. Systems that use rules-based or machine learning algorithms to find malicious URLs usually rely on feature engineering. This requires domain expertise and experience. Sometimes, even after extracting features from a dataset, it may not completely leverage the potential of the dataset. The proposed method employs Natural Language Processing (NLP) approaches to vectorize the words in the URLs and applies machine learning and deep learning models for classification. Vectorization technique in NLP reduces the effort of feature engineering and maximizing the use of the dataset. For the experiment, two separate datasets are used. To vectorize the URL text, three different vectorization methods are used. To evaluate the performance of the proposed method, two different datasets (D1 and D2) that are regularly utilized in the research domain were used. The results demonstrate that the superior accuracy of 92.4 % with the D1 dataset is achieved by the Decision Tree (DT) with count vectorizer and the Random Forest (RF) with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer. With the D2 dataset, DT with TF-IDF vectorizer obtains a greater accuracy of 99.5 %. The Artificial Neural Network (ANN) model achieves 89.6 % accuracy with the D1 dataset and 99.2 % accuracy with the D2 dataset.

Keywords: malicious domain, phishing URL, NLP, machine learning, deep learning, ANN, CNN

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