Detection of phishing websites using an efficient feature-based machine learning framework in python

 

Detection of phishing websites using an efficient feature-based machine learning framework

 

 PROJECT ID: PYTHON09

 

PROJECT NAME: Detection of phishing websites using an efficient feature-based machine learning framework

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number etc. Attackers fool the Internet users by masking webpage as a trustworthy or legitimate page to retrieve personal information. There are many anti-phishing solutions such as blacklist or whitelist, heuristic and visual similarity-based methods proposed to date, but online users are still getting trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques. Our model has been evaluated using eight different machine learning algorithms and out of which, the Random Forest (RF) algorithm performed the best with an accuracy of 99.31%. The experiments were repeated with different (orthogonal and oblique) random forest classifiers to find the best classifier for the phishing website detection. Principal component analysis Random Forest (PCA-RF) performed the best out of all oblique Random Forests (oRFs) with an accuracy of 99.55%. We have also tested our model with the third-party-based features and without third-party-based features to determine the effectiveness of third-party services in the classification of suspicious websites. We also compared our results with the baseline models (CANTINA and CANTINA+). Our proposed technique outperformed these methods and also detected zero-day phishing attacks.

 

Experimental Environment and Parameter Setup

In this paper, DBN experiments are conducted in stand-alone mode. The hardware environment includes CPU processor Intel i5-4570 quad-core, 16G memory, and the Nvidia GeForce series GTX760 graphics card. Deep learning algorithms often require high computational performance. Many popular deep learning libraries use the GPU to increase computation speed.

GPUMLib [78] is a GPU machine learning library. It may use C++ and Compute Unified Device Architecture (CUDA) and has support for Backpropagation (BP), Multiple Backpropagation (MBP), Autonomous Training System (ATS) for creating BP and MBP networks, Neural Selective Input Model (NSIM) for BP and MPB, RBM, SVM, and other computationally machine learning algorithms. SVM model can be seen as a shallow feature extraction (with a hidden layer). DBN selects at least two layers in order to relatively enhance the feature selection effect, and too many layers will lead to overfitting. DBN main module declaration is as in Listing

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

CONCLUSION

In this paper, we analyze the features of phishing websites and present two types of feature for web phishing detection: original feature and interaction feature. Then we introduce DBN to detect phishing websites and discuss the detection model and algorithm for DBN. We train DBN and get the appropriate parameters for detection in the small data set. In the end, we use the big data set to test DBN and TPR is approximately 90%.

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

PROJECT SOFWARE	ZIP
PROJECT REPORT PAGE	60 -80 Pages
CAN BE USED IN	Marketing (MBA)
PROJECT COST	1500/- Only
PDF SYNOPSIS COST	250/- Only
PPT PROJECT COST	300/- Only
PROJECT WITH SPIRAL BINDING	1750/- Only
PROJECT WITH HARD BINDING	1850/- Only
TOTAL COST (SYNOPSIS, SOFTCOPY, HARDBOOK, and SOFTWARE, PPT)	2500/- Only
DELIVERY TIME	1 OR 2 Days (In case Urgent Call: 8830288685)
SUPPORT / QUERY	www.projectsready.in
CALL	8830288685
Email	help@projectsready.in
[Note: We Provide Hard Binding and Spiral Binding only Nagpur Region]	Download