| Titre : | Cyber-Attacks prediction using Data Mining technics |
| Auteurs : | Mohamed Sedrata, Auteur ; Salima Berima, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2022 |
| Format : | 1 vol. (53 p.) / couv. ill. en coul / 30 cm |
| Langues: | Anglais |
| Mots-clés: | Cyber attacks, Linear regression, prediction , data processing, training, testing |
| Résumé : |
Cyberattacks are the most common concern right now, which is very much about diversion. If a person does not have a suitable security system, linked information can be hacked easily. One of the most frequent causes of cyberattacks is because of intruders. Therefore, it has enhanced the security process by using machine learning algorithms and prediction and with the help of artificial intelligence (AI) to avoid cyber attacks. In this study, we will focus on the precision and approximation of the correct result in case of attack or not through the use of machine learning algorithms, so there are ways to detect and prevent attacks and protect them from attackers like IDS, IPS, Firewall and They just reduce and don’t get the job done. We propose protective methods such as machine learning, we gonna use Linear regression to predict data that allows the machine to learn and predict using the data we study, and we specifically propose a Linear regression algorithm for analyzing those inputs, where we propose a dataset imported from the Kaggle Internet with the addition of data with a suggestion a template for including normal and unusual transactions. |
| Sommaire : |
General introduction 5 1 Cybersecurity 6 1.1 Introduction 6 1.2 Objectives . 6 1.2.1 Why is cybersecurity important ? 7 1.2.2 What Are the fundamentals of Information Security ? . 7 1.3 Definition of cyber attack .. 8 1.4 Causes and damage 8 1.5 Types of cyber attack . 9 1.6 Related works . 11 1.7 Defense mechanisms 11 1.7.1 Definition Intrusion Detection System 12 1.7.2 Definition Firewall 12 1.8 conclusion 13 2 Data Mining 14 2.1 Introduction . 14 2.2 What is Data Mining 14 2.2.1 Data Mining Objectives 15 2.2.2 What future for Data Mining? 17 2.2.3 Advantages of Data Mining 17 2.3 Where We can apply Data Mining . 17 2.3.1 Future Healthcare : 17 2.3.2 Intrusion Detection :18 2.3.3 Financial Banking : . 18 2.3.4 Criminal Investigation : 18 2.3.5 Cyber security 18 2.4 The process of Data Mining 19 2.5 The Technics of Data Mining 20 i2.6 Regression . 22 2.6.1 Types of Regression Analysis Techniques 23 2.6.2 1. Linear Regression 23 2.6.3 Formulation of Linear Regression Technique 24 2.6.4 Algorithm of Linear Regression Technique. 25 2.6.5 2. Logistic Regression 25 2.6.6 What is the purpose of logistic regression? 26 2.6.7 Advantages and disadvantages of logistic regression . 26 2.7 conclusion 27 3 Conception 28 3.1 Introduction 28 3.2 System presentation 28 3.2.1 System objectives . 28 3.2.2 Global System Architecture 29 3.3 Detailed System Design . 30 3.3.1 Data preprocessing 31 3.3.2 Data Collection 32 3.3.3 Design by linear regression Algorithm 33 3.4 Conclusion . 36 4 Implementation 37 4.1 Introduction 37 4.2 Development Environment 37 4.2.1 development tools 37 4.2.2 Python . 38 4.2.3 Environment used for creating the model 38 4.2.4 Spyder 39 4.3 Data Structures 40 4.3.1 part of dataset used 40 4.4 Environment Setup 42 4.4.1 Linear regression Algorithm 42 4.4.2 Work steps with pictures . 44 4.5 Conclusion . 52 Conclusion g´en´erale 53 |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| MINF/713 | Mémoire master | bibliothèque sciences exactes | Consultable |
