Tytuł:

Enhancing Cloud-Hosted Phishing Detection Using RNN-LSTM: A Deep Learning Framework, Journal of Telecommunications and Information Technology, 2025

Tytuł publikacji grupowej:

2025, nr 1, JTIT-artykuły

Autor:

Senouci, Oussama ; Benaouda, Nadjib

Temat i słowa kluczowe:

cloud services ; cybersecurity ; deep learning ; phishing detection ; RNN-LSTM

Opis:

kwartalnik

Abstrakt:

Phishing attacks targeting cloud computing services are more sophisticated and require advanced detection mechanisms to address evolving threats. This study introduces a deep learning approach leveraging recurrent neural networks (RNNs) with long short-term memory (LSTM) to enhance phishing detection. The architecture is designed to capture sequential and temporal patterns in cloud interactions, enabling precise identification of phishing attempts. The model was trained and validated using a dataset of 10,000 samples, adapted from the PhishTank repository. This dataset includes a diverse range of attack vectors and legitimate activities, ensuring comprehensive coverage and adaptability to real-world scenarios. The key contribution of this work includes the development of a high-performance RNN-LSTM-based detection mechanism optimized for cloud-specific phishing patterns that achieve 98.88% accuracy. Additionally, the model incorporates a robust evaluation framework to assess its applicability in dynamic cloud environments. The experimental results demonstrate the effectiveness of the proposed approach, surpassing existing methods in accuracy and adaptability.

Numer:

1

Wydawca:

National Institute of Telecommunications

Data wydania:

2025, nr 1

Typ zasobu:

artykuł

DOI:

10.26636/jtit.2025.1.1916

eISSN:

on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Licencja:

CC BY 4.0