Suspicious Activity Detection Model in Bank Transactions using Deep Learning with Fog Computing Infrastructure

DOI

10.2991/978-94-6463-471-6_29How to use a DOI?

Keywords

Cyber attack; Suspicious activity detection; Machine learning. Deep learning; Temporal data; Bio-inspired algorithm

Abstract

The banking sectors are facing several challenges in detecting and preventing different types of cyber attacks. The main challenge is to find the suspicious activities in money transactions. The majority of Financial institutions are commercial banks suffers lot due to these cyber attacks. The time critical applications requires very small latency in providing services and Cloud computing infrastructures are not well-suited for time-sensitive applications as it takes more latency. Thus, fog computing, an innovative computing paradigm, is utilized to reduce communication latency. The tradition, statistical and machine learning methods effectively identified suspicious activity, but accuracy and trade off between recall and precision is very less. IN this paper a novel suspicious activity detection model is proposed using deep learning with nature-inspired algorithm, to improve the accuracy. The proposed approach analyses transactional patterns in historical data and classify the suspicious and non-suspicious actions. The simulation model is developed using Python programming language and the Google Colab framework to evaluate proposed model. The simulation results show improved accuracy compared to existing state of art works.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Girish Wali
AU  - Chetan Bulla
PY  - 2024
DA  - 2024/07/30
TI  - Suspicious Activity Detection Model in Bank Transactions using Deep Learning with Fog Computing Infrastructure
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 292
EP  - 302
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_29
DO  - 10.2991/978-94-6463-471-6_29
ID  - Wali2024
ER  -