Evading obscure communication from spam emails

Khan Farhan Rafat; Qin Xin; Abdul Rehman Javed; Zunera Jalil; Rana Zeeshan Ahmad; Khan Farhan Rafat; Qin Xin; Abdul Rehman Javed; Zunera Jalil; Rana Zeeshan Ahmad

doi:10.3934/mbe.2022091

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 1926-1943. doi: 10.3934/mbe.2022091

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Research article Special Issues

Evading obscure communication from spam emails

1.
Department of Cyber Security, Faculty of Computing and AI, Air University, PAF Complex, E-9, Islamabad, Pakistan
2.
Faculty of Science and Technology, University of the Faroe Islands, Vestarabryggja 15, FO 100, Torshavn, Faroe Islands
3.
Department of Information Technology, University of Sialkot, Pakistan

Academic Editor: Thippa Reddy Gadekallu

Received: 02 October 2021 Accepted: 28 November 2021 Published: 22 December 2021

Spam is any form of annoying and unsought digital communication sent in bulk and may contain offensive content feasting viruses and cyber-attacks. The voluminous increase in spam has necessitated developing more reliable and vigorous artificial intelligence-based anti-spam filters. Besides text, an email sometimes contains multimedia content such as audio, video, and images. However, text-centric email spam filtering employing text classification techniques remains today's preferred choice. In this paper, we show that text pre-processing techniques nullify the detection of malicious contents in an obscure communication framework. We use Spamassassin corpus with and without text pre-processing and examined it using machine learning (ML) and deep learning (DL) algorithms to classify these as ham or spam emails. The proposed DL-based approach consistently outperforms ML models. In the first stage, using pre-processing techniques, the long-short-term memory (LSTM) model achieves the highest results of 93.46% precision, 96.81% recall, and 95% F1-score. In the second stage, without using pre-processing techniques, LSTM achieves the best results of 95.26% precision, 97.18% recall, and 96% F1-score. Results show the supremacy of DL algorithms over the standard ones in filtering spam. However, the effects are unsatisfactory for detecting encrypted communication for both forms of ML algorithms.
- Email classification,
- spam,
- ham,
- machine learning,
- stenography,
- text pre-processing
Citation: Khan Farhan Rafat, Qin Xin, Abdul Rehman Javed, Zunera Jalil, Rana Zeeshan Ahmad. Evading obscure communication from spam emails[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1926-1943. doi: 10.3934/mbe.2022091

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Abstract

Spam is any form of annoying and unsought digital communication sent in bulk and may contain offensive content feasting viruses and cyber-attacks. The voluminous increase in spam has necessitated developing more reliable and vigorous artificial intelligence-based anti-spam filters. Besides text, an email sometimes contains multimedia content such as audio, video, and images. However, text-centric email spam filtering employing text classification techniques remains today's preferred choice. In this paper, we show that text pre-processing techniques nullify the detection of malicious contents in an obscure communication framework. We use Spamassassin corpus with and without text pre-processing and examined it using machine learning (ML) and deep learning (DL) algorithms to classify these as ham or spam emails. The proposed DL-based approach consistently outperforms ML models. In the first stage, using pre-processing techniques, the long-short-term memory (LSTM) model achieves the highest results of 93.46% precision, 96.81% recall, and 95% F1-score. In the second stage, without using pre-processing techniques, LSTM achieves the best results of 95.26% precision, 97.18% recall, and 96% F1-score. Results show the supremacy of DL algorithms over the standard ones in filtering spam. However, the effects are unsatisfactory for detecting encrypted communication for both forms of ML algorithms.

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