Enhancing Phishing Detection in Cloud Environments Using RNN-LSTM in a Deep Learning Framework

• Autorzy: Senouci Oussama , Benaouda Nadjib

Identyfikatory

DOI

10.26636/jtit.2025.1.1916

• Języki publikacji: EN

Abstrakty

EN

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 preciseidentification of phishing attempts. The model was trained andvalidated 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 keycontribution 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.

Słowa kluczowe

EN

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

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2025
• Tom: nr 1
• Strony: 1--9
• Opis fizyczny: Bibliogr. 16 poz., rys., wykr.

Twórcy

autor

Senouci Oussama

• University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria

• https://orcid.org/0000-0002-5345-0713

autor

Benaouda Nadjib

• University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria

• https://orcid.org/0000-0002-4361-9597

Bibliografia

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