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

Senouci Oussama, Benaouda Nadjib

Journal of Telecommunications and Information Technology

2025

nr 1

1--9

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.

Efficient Routing for Delay-energy Tradeoff in Event-based Wireless Sensor Networks

Benaouda Nadjib

Journal of Telecommunications and Information Technology

2024

nr 4

69--76

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) by providing a foundation for collecting, transmitting and processing data from the physical world. Beyond the necessity of proposing solutions that are in line with the constrained resources of sensor nodes, particularly their limited energy capacity, the consideration of real-time data collection becomes essential. This is particularly vital due to the fact that many IoT applications require timely data collection. However, the need to establish energy-efficient routes contradicts the requirement to guarantee timely data collection. Hence, achieving an equilibrium and striking, subsequently, a trade-off between these two issued becomes imperative. To answer this question, a localized delay-bounded and energy-efficient routing protocol (abbreviated as LDER) is presented. It is based on another protocol, namely DEDA, aimed at achieving a higher energy conservation degree. To validate the efficacy of LDER, simulations were conducted using the J-sim simulator. The results demonstrate the ability of LDER to achieve the desired equilibrium and prove its superiority over DEDA.