Review of Cybercrime Detection Approaches using Machine Learning and Deep Learning Techniques
Authors:
- 5 June 2024
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 772-779
Abstract
This research explores the use of deep learning and Machine Learning techniques in cybersecurity to detect and mitigate cyber threats. Specifically, this study focuses on Long Short-Term Memory (LSTM) and Graph Neural Networks (GNN) to analyse network traffic and system logs for patterns, anomalies, and malicious activities. The goal of the proposed research work is to enhance cyber-defining mechanisms and network security by leveraging Artificial Intelligence (AI) to combat the evolving cyber threats.Keywords
This publication has 11 references indexed in Scilit:
- An intelligent cyber security phishing detection system using deep learning techniquesCluster Computing, 2022
- Machine Learning and Deep Learning Approaches for CyberSecurity: A ReviewIEEE Access, 2022
- Detection of E-Mail Phishing Attacks – using Machine Learning and Deep LearningInternational Journal of Computer Applications, 2022
- C SVM Classification and KNN Techniques for Cyber Crime DetectionWireless Communications and Mobile Computing, 2022
- Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime LandscapeIEEE Access, 2021
- Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber SecurityInternational Journal of Interactive Mobile Technologies (iJIM), 2020
- Comprehensive Review of Cybercrime Detection TechniquesIEEE Access, 2020
- Computational System to Classify Cyber Crime Offenses using Machine LearningSustainability, 2020
- Diverging deep learning cognitive computing techniques into cyber forensicsForensic Science International: Synergy, 2019
- Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter networkComputers in Human Behavior, 2016