Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection

Version 1 : Received: 31 January 2022 / Approved: 2 February 2022 / Online: 2 February 2022 (09:29:22 CET)

How to cite: Ulfath, R.E.; Alqahtani, H.; Hammoudeh, M.; Sarker, I.H. Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection. Preprints 2022, 2022020024 (doi: 10.20944/preprints202202.0024.v1). Ulfath, R.E.; Alqahtani, H.; Hammoudeh, M.; Sarker, I.H. Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection. Preprints 2022, 2022020024 (doi: 10.20944/preprints202202.0024.v1).

Abstract

Smartphones are prone to SMS phishing due to the rapid growth in the availability of smart mobile technologies driven by Internet connections. Also, detecting phishing SMS is a challenging task due to the unstructured nature of SMS text data with non-linear complex correlations. In this concern, considering the recent advancements in the domain of cybersecurity, we have proposed a hybrid deep learning framework that extracts robust features from SMS texts followed by an automatic detection of Phishing SMS. Due to combining the potential capability of individual models into one hybrid framework, it has outperformed various other individual machine learning and deep learning models. The proposed Phishing Detection framework is an effective hybrid combination of pretrained transformer model, MPNet (Masked and Permuted Language Modeling), with supervised ConvNets (CNN) and Bi-directional Gated Recurrent Units (GRU). It is intended to successfully detect unstructured short phishing text messages that contain complex patterns.

Keywords

Smishing; Deep learning; NLP; AI; Cybersecurity

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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