Apache Spark Based Deep Learning for Social Transaction Analysis

Raouf Jmal, Mariam Masmoudi, Mariam Masmoudi, Ikram Amous, Corinne Zayani, Florence Sèdes

2023

Abstract

In an attempt to cope with the increasing number of trust-related attacks, a system that analyzes the whole social transaction in real-time becomes a necessity. Traditional systems cannot analyze transactions in real-time and most of them use machine learning approaches, which are not suitable for the real-time processing of social transactions in the big data environment. Therefore, in this paper, we propose a novel deep learning detection system based on Apache Spark that is capable of handling huge transactions and streaming batches. Our model is made up of two main phases: the first phase builds a supervised deep learning model to classify transactions (either benign transactions or malicious transactions). The second phase aims to analyze transaction streams using spark streaming, which transforms the model to batches of data in order to make predictions in real-time. To verify the effectiveness of the proposed system, we implement this system and we perform several comparison experiments. The obtained results show that our approach has achieved more satisfactory efficiency and accuracy, compared to other works in the literature. Thus, it is very suitable for real-time detection of malicious transactions with large capacity and high speed.

Paper Citation

in Harvard Style

Jmal R., Masmoudi M., Amous I., Zayani C. and Sèdes F. (2023). Apache Spark Based Deep Learning for Social Transaction Analysis. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 365-372. DOI: 10.5220/0012202600003584

in Bibtex Style

@conference{webist23,
author={Raouf Jmal and Mariam Masmoudi and Ikram Amous and Corinne Zayani and Florence Sèdes},
title={Apache Spark Based Deep Learning for Social Transaction Analysis},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={365-372},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012202600003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Apache Spark Based Deep Learning for Social Transaction Analysis
SN - 978-989-758-672-9
AU - Jmal R.
AU - Masmoudi M.
AU - Amous I.
AU - Zayani C.
AU - Sèdes F.
PY - 2023
SP - 365
EP - 372
DO - 10.5220/0012202600003584
PB - SciTePress