Python
Cyber Security
A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
Aim:
People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. The main aim is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms.
A Novel Multipurpose Watermarking Scheme Capable of Protecting and Authenticating Images with Tamper Detection and Localisation Abilities
An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier
BSFR-SH: Blockchain-Enabled Security Framework against Ransomware Attacks for Smart Healthcare
Chaotic Image Encryption Using Piecewise Logistic Sine Map
Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach
DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform
Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
Fraud Detection in Banking Data by Machine Learning Technique
Obfuscated Privacy Malware Classification Using Machine Learning and Deep Learning Techniques
Python, Cybersecurity, Deep Learning, Machine Learning, Artificial Intelligence, Cyber Security, Deep Learning, Machine Learning
Aim
The aim of this research is to develop an intelligent system capable of detecting and classifying obfuscated privacy malware into various categories and families. This system leverages machine learning and deep learning models trained on the CIC-MalMem-2022 dataset to improve accuracy and address the challenges posed by data imbalance and complex malware behaviour.
