Malware detection and analysis using machine learning
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives of the Project<br>2. Malware Detection Techniques<br> 2.1 Signature-based Detection Methods<br> 2.2 Behavior-based Detection Approaches<br>3. Machine Learning Models for Malware Analysis<br> 3.1 Feature Extraction and Selection<br> 3.2 Classification Algorithms for Malware Detection<br>4. Dataset Collection and Preprocessing<br> 4.1 Malware Samples and Ground Truth Labels<br> 4.2 Data Cleaning and Feature Engineering<br>5. Evaluation Metrics and Performance Analysis<br> 5.1 Accuracy, Precision, and Recall<br> 5.2 False Positive Rate and False Negative Rate<br></p>
Project Abstract
This project aims to develop and evaluate machine learning-based techniques for the detection and analysis of malware in computer systems. The project will explore the use of supervised and unsupervised learning algorithms to identify and classify malicious software based on behavioral patterns, code analysis, and network traffic. The project will also investigate the challenges and limitations of machine learning approaches in the context of evolving malware threats.
Project Overview
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