Obfuscated Privacy Malware Classification Using Machine Learning and Deep Learning Techniques

5,500.00
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.

Phishing Detection System through Hybrid Machine Learning Based on URL

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā  The aim of this research is to develop an advanced phishing detection system that leverages a hybrid machine

Phishing URL Detection: A Real-Case Scenario Through Login URLs

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā  To provide an automated system for the recognition of phishing websites through login URLs Abstract: Ā Ā Ā Ā Ā Ā Ā Ā Ā  Phishing attacks

Ransomware Classification and Detection with Machine Learning Algorithms

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā  This study aims to improve the accuracy of Ransomware Classification and Detection with Machine Learning Algorithms Ransomware Classification,

Social Media Forensics an Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks with Uncertainty

5,500.00
Aim: To propose an approach that improves the accuracy and efficiency of cyberbullying detection in social media text by utilizing an advanced model that aims to overcome ambiguity and classification challenges.