Title: Monitor and detect suspicious online transactions

Authors: Swagata Sarkar; R. Babitha Lincy; P. Sasireka; Sonam Mittal

Addresses: Artificial Intelligence and Data Science Department, Sri Sairam Engineering College, West Tambaram, Chennai, India ' Computer and Communication Engineering Department, Sri Eshwar College of Engineering, Kinathukadavu, Coimbatore, India ' Department of Electronics and Communication Engineering, SA Engineering College, Chennai, India ' Department of Computer Science and Engineering, Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India

Abstract: This article provides a thorough examination of phishing attempts, their use, several contemporary visual similarity-based phishing detection systems, and their comparison evaluation. This research article aims to propose an effective design technique for IDS with regard to online applications. We develop a new set of features based on time-frequency analytics that makes use of 2-D models of monetary operations for preventing money laundering systems. As a classification algorithm, random forest is used, and clustering algorithm is used to tune the hyperparameters. Our findings imply that bitcoin exchanges would behave in an excessive reporting manner more than private banks under this law. We specifically take into account the monetary operations as a digital signal and attempt to build a classifier using a collection of frequently mined rules. Our tests on a replicated transaction dataset based on actual banking operations demonstrate the effectiveness of our suggested approach.

Keywords: random forest technique; time frequency research; graphical study.

DOI: 10.1504/IJESDF.2023.133967

International Journal of Electronic Security and Digital Forensics, 2023 Vol.15 No.6, pp.632 - 643

Received: 15 Jul 2022
Accepted: 27 Oct 2022
Published online: 06 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article