About this project
This project builds a network intrusion detection system (NIDS) that classifies network traffic as benign or attack using machine learning. The model is trained on NSL-KDD/CICIDS and exposed via a dashboard that flags suspicious packets in near real-time.
Suggested tech stack
- Python
- scikit-learn
- Wireshark
- Flask
Chapters 1–5 outline
Chapter 1
Introduction: background to ML-based intrusion detection, statement of the problem (Nigerian SMEs lack affordable cybersecurity tools and suffer rising attacks), aim and objectives, research questions, scope, significance of the study, and definition of terms.
Chapter 2
Literature Review: theoretical framework, review of related works on ML-based intrusion detection, gaps in existing studies, and a summary positioning this project.
Chapter 3
Methodology / System Analysis and Design: Python + scikit-learn / TensorFlow + Wireshark + Flask. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).
Chapter 4
Implementation and Results: a working IDS with performance benchmarks (accuracy, F1) and live packet capture demo. Presentation of findings, testing, evaluation, and discussion of results.
Chapter 5
Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.
Get this project done — chapters, code, defence support
Final Year writes the full project for you. Original content, on time, with chat support up to defence day.
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