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Introduction:

The rapid advancement of technology has brought about numerous benefits, but it has also led to the rise of cyber threats such as malware. Malware, short for malicious software, poses a significant risk to individuals and organizations by compromising the security and integrity of their systems. Traditional methods of malware detection and classification rely on signature-based approaches, which are limited in their ability to detect new and unknown malware variants. In recent years, artificial intelligence (AI) has emerged as a promising solution to enhance malware detection and classification capabilities.

Chapter One:

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review

2.1 Evolution of Malware
2.2 Traditional Methods of Malware Detection
2.3 Limitations of Traditional Approaches
2.4 Introduction to Artificial Intelligence
2.5 AI Applications in Cybersecurity
2.6 AI-Powered Malware Detection Techniques
2.7 Existing AI-Based Malware Detection Systems
2.8 Comparison of AI and Traditional Methods
2.9 Challenges in AI-Powered Malware Detection
2.10 Future Trends in AI-Powered Malware Detection

Chapter Three: System Design and Methodology

3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction
3.4 Machine Learning Algorithms
3.5 Model Training and Testing
3.6 Performance Evaluation Metrics
3.7 Cross-validation Techniques
3.8 Implementation of the Proposed System

Chapter Four: System Implementation

4.1 Selection of Tools and Technologies
4.2 Data Acquisition
4.3 Feature Engineering
4.4 Model Development
4.5 Model Evaluation
4.6 System Testing
4.7 Performance Tuning
4.8 Results and Analysis

Chapter Five: Conclusion and Summary

In conclusion, this thesis aims to demonstrate the effectiveness of AI-powered techniques in detecting and classifying malware. By leveraging the power of artificial intelligence, we can enhance the accuracy and efficiency of malware detection systems, thereby improving cybersecurity defenses. The insights and findings from this research will contribute to the ongoing efforts to combat the evolving threat landscape of malware.

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