Showing all 10 results

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

5,500.00
Aim:            People can use credit cards for online transactions as it provides an efficient and easy-to-use facility.  With the

An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier

5,500.00
Aim:             The aim of this study is to enhance the efficacy of Cloud Intrusion Detection Systems by proposing an

Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach

5,500.00
Aim:            Massive adoption of mobile money in countries, the global transaction value of   mobile money exceeded $2 billion in

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

5,500.00
Aim:             Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social

Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish

5,500.00
Aim:           Cyber bulling is described as the serious, intentional, and repetitive acts of a person’s cruelty toward others using

Fraud Detection in Banking Data by Machine Learning Technique

5,500.00
Aim:           The aim is to leverage the power of machine learning to create efficient and accurate fraud detection systems

LSTM Based Phishing Detection for Big Email Data

5,500.00
Aim:           Cybersecurity incidents have occurred frequently. Attackers have used phishing emails as a knock-on to successfully invade government systems.

Phishing Detection System through Hybrid Machine Learning Based on URL

5,500.00
Aim:          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:              To provide an automated system for recognition the real-case Scenario through login URLs Abstract:            Phishing is a

Ransomware Classification and Detection with Machine Learning Algorithms

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