Intrusion detection using Decision Tree algorithm in python

 

Intrusion detection using Decision Tree algorithm

 

 PROJECT ID: PYTHON14

 

PROJECT NAME: Intrusion detection using Decision Tree algorithm

 

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

 

PROJECT ABSTRACT:

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

Fog computing [1, 2] was defined as a highly virtualized computing platform for migrating cloud computing center tasks to network edge devices. Fog computing provides computing, storage, and networking service between mobile users and traditional Cloud platform, which is complementary to Cloud. The fog computing introduces the middle layer between the cloud and the mobile users, extending the cloud-based network architecture [3–6]. A basic fog framework is shown in Figure 1, each mobile user is connected to one of the fog nodes. Meanwhile, fog nodes could be interconnected with each other and are linked to the Cloud [7]. The fog computing reduces unnecessary multiple communication between the cloud computing center and the mobile users. For instance, when the number of users has increased dramatically, these users can obtain the service by visiting the contents of the cache in the fog servers so as to reduce network delay [8]. And it also significantly reduces the bandwidth of the backbone link load [9, 10]. Unfortunately, the nodes in fog environment are close to the mobile users, and fog computing nodes are usually composed of devices with weak computing ability. Traditional network attacks are widely presented in fog environment; fog devices may face network security challenges. However, Intrusion Detection Systems (IDS) can be used for fog environment

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:

Tradition network attacks are widely present in fog computing environment. Although the IDS in tradition network have been well investigated, unfortunately directly use of them in fog computing environment may not inappropriate. In this study, we propose a system based on the decision tree, multimethods are compared with this one, not only the 10% dataset but also the full dataset is tested, and the experiment results show that our system is effective. In addition, we also compared the detection time for each method. In the case of guaranteed accuracy, although the decision tree time is not the best one, the calculation time is also acceptable. Above all, our IDS system can be used in fog computing environment over big data. In our future, we will engage in the research of the IDS for other kinds of attacks.

 

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          

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