Showing 1–12 of 13 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

A Novel Multipurpose Watermarking Scheme Capable of Protecting and Authenticating Images with Tamper Detection and Localisation Abilities

5,500.00
Abstract:             In this paper, a digital watermarking method is proposed which is based on Contourlet wavelet transform (CT) and

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

BSFR-SH: Blockchain-Enabled Security Framework against Ransomware Attacks for Smart Healthcare

5,500.00
Aim:             Our study aims to introduce a Blockchain-Enabled Security Framework against Ransomware Attacks using Machine Learning to ensure high

Chaotic Image Encryption Using Piecewise Logistic Sine Map

5,500.00
Aim:             To analyze the Arnold cat map and demonstrating its effectiveness in the image encryption. Synopsis:             With the

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

5,500.00
Aim:    Predict the Swahili smishing attack. Mobile money platform evolution could be attributed to the bureaucracy of owning a

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

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

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:          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 the recognition of phishing websites through login URLs Abstract:           Phishing attacks