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

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

Android Malware Detection Using Informative Syscall Subsequences

5,500.00
Aim: To develop a robust and efficient system for detecting Android malware by leveraging informative syscall subsequences, advanced machine learning, and deep learning models trained on the CICMalDroid2020 dataset.

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

E-Commerce Fraud Detection Using Generated Data From BANKSIM Using Machine Learning

5,500.00
Aim To develop a robust fraud detection system for e-commerce transactions by leveraging machine learning algorithms on simulated BANKSIM data, achieving high classification accuracy to mitigate risks associated with fraudulent transactions.

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

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.

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