Abstract:
This paper presents the design and development of an algorithm for network intrusion detection. With the increasing number of cyber threats and attacks, it has become crucial to have robust intrusion detection systems in place. The proposed algorithm aims to enhance the accuracy and efficiency of detecting network intrusions, thereby improving the overall security of computer networks. The algorithm utilizes machine learning techniques and anomaly detection methods to identify and classify various types of network intrusions. The results of the algorithm’s performance are evaluated using real-world network traffic data, demonstrating its effectiveness in detecting and mitigating potential security breaches.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations
1.5 Organization of the Paper

Chapter 2: Literature Review
2.1 Network Intrusion Detection Systems
2.2 Machine Learning Techniques for Intrusion Detection
2.3 Anomaly Detection Methods
2.4 Existing Algorithms and Approaches
2.5 Summary

Chapter 3: Methodology
3.1 Data Collection and Preprocessing
3.2 Feature Selection and Extraction
3.3 Algorithm Design and Implementation
3.4 Evaluation Metrics
3.5 Summary

Chapter 4: Experimental Results
4.1 Dataset Description
4.2 Performance Evaluation
4.3 Comparison with Existing Algorithms
4.4 Discussion of Results
4.5 Summary

Chapter 5: Conclusion and Future Work
5.1 Summary of Findings
5.2 Contributions
5.3 Limitations and Challenges
5.4 Future Directions
5.5 Conclusion

References