Introduction
The advancement of technology has paved the way for the development of sophisticated software systems that have revolutionized various industries. However, with the increasing complexity of software, the threat of malicious code has also become more prominent. Malicious code, also known as malware, poses a significant risk to the security and integrity of software systems, leading to data breaches, financial loss, and reputation damage for organizations. Traditional methods of detecting malicious code are no longer sufficient to combat the evolving tactics of cybercriminals. As a result, there is a growing interest in leveraging Artificial Intelligence (AI) techniques to enhance the detection and prevention of malicious code in software.
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 Overview of Malicious Code
2.2 Traditional Methods of Malicious Code Detection
2.3 Artificial Intelligence in Cybersecurity
2.4 Machine Learning Techniques for Malicious Code Detection
2.5 Deep Learning Approaches for Malicious Code Detection
2.6 Challenges in Detecting Malicious Code
2.7 Case Studies on AI-Based Malicious Code Detection
2.8 Comparison of AI Techniques for Malicious Code Detection
2.9 Future Trends in AI-Based Malicious Code Detection
2.10 Summary of Literature Review
Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 AI Models for Malicious Code Detection
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Ethical Considerations
3.8 Limitations of the Study
Chapter Four: Discussion of Findings
4.1 Analysis of AI Models for Malicious Code Detection
4.2 Performance Evaluation of AI Techniques
4.3 Comparison with Traditional Methods
4.4 Interpretation of Results
4.5 Implications for Practice
4.6 Recommendations for Future Research
Chapter Five: Conclusion and Summary
5.1 Recap of Research Objectives
5.2 Summary of Findings
5.3 Contribution to Knowledge
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Recommendations for Further Research
5.7 Conclusion
Thesis Overview: The Role of AI in Detecting Malicious Code in Software
The thesis aims to investigate the role of Artificial Intelligence (AI) in detecting malicious code in software systems. The research will review the existing literature on malicious code detection, traditional methods, and AI techniques. The study will employ a research methodology that includes data collection, AI model development, and performance evaluation. The findings will be discussed in detail, highlighting the effectiveness of AI in detecting malicious code. The conclusion will summarize the key findings, contributions to knowledge, practical implications, limitations, and recommendations for future research in the field of AI-based malicious code detection.
