Introduction:
The rise of malicious code in software has become a major concern for organizations and individuals alike. With the increasing complexity of software systems and the ever-evolving nature of cyber threats, traditional methods of detecting and preventing malicious code are no longer sufficient. This has led to the exploration of new technologies such as Artificial Intelligence (AI) to aid in the detection of malicious code in software.
This thesis aims to explore the role of AI in detecting malicious code in software, examining the potential benefits and challenges that come with this technology. By understanding how AI can be used to enhance the security of software systems, organizations can better protect themselves from cyber threats and prevent potential data breaches.
Chapter One: Introduction
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 Overview of malicious code in software
2.2 Traditional methods of detecting malicious code
2.3 Introduction to Artificial Intelligence
2.4 Applications of AI in cybersecurity
2.5 AI algorithms for detecting malicious code
2.6 Challenges of using AI for detecting malicious code
2.7 Case studies of AI in detecting malicious code
2.8 Comparison of AI vs traditional methods
2.9 Future trends in AI for cybersecurity
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 Sample selection
3.5 Ethical considerations
3.6 Validity and reliability
3.7 Research limitations
3.8 Research instruments
Chapter Four: Discussion of Findings
4.1 Overview of findings
4.2 Analysis of data collected
4.3 Comparison of AI algorithms
4.4 Implications for practice
4.5 Recommendations for future research
4.6 Limitations of the study
4.7 Conclusions drawn from findings
Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications for practice
5.3 Recommendations for organizations
5.4 Contributions to the field
5.5 Reflections on the research process
5.6 Limitations of the study
5.7 Suggestions for future research
5.8 Conclusion
Thesis Overview:
The role of AI in detecting malicious code in software is a critical topic in the field of cybersecurity. This thesis aims to explore how AI can be used to enhance the detection of malicious code in software systems, providing organizations with an additional layer of security against cyber threats.
Chapter one provides an introduction to the topic, outlining the background of the study, the problem statement, objectives, limitations, scope, and significance of the study. It also includes the structure of the thesis and definitions of key terms.
Chapter two presents a comprehensive literature review on malicious code in software, traditional detection methods, the role of AI in cybersecurity, AI algorithms for detecting malicious code, challenges, case studies, and future trends in AI for cybersecurity.
Chapter three details the research methodology used in this study, including research design, data collection methods, analysis techniques, sample selection, ethical considerations, and research limitations.
Chapter four discusses the findings of the study, including an overview of findings, analysis of data collected, comparison of AI algorithms, implications for practice, recommendations for future research, limitations, and conclusions drawn from the findings.
Chapter five concludes the thesis with a summary of key findings, implications for practice, recommendations for organizations, contributions to the field, reflections on the research process, limitations of the study, suggestions for future research, and a final conclusion.
