AI-Powered Real-Time Network Intrusion System
Budget / Salary₹1,500–12,500
TypeFreelance project
LocationRemote
Posted1 hour ago
PROJECT
AI-Powered Network Intrusion Detection System (NIDS)
Project Objective
Build a machine learning-based NIDS that classifies network traffic as normal or malicious in real time, detects attack types (DDoS, port scan, brute force, SQL injection traffic), generates alerts, and produces a visual dashboard — mimicking what enterprise SOC tools like Darktrace and Securonix do.
Project Details Table
Attribute Details
Full Title AI-Powered Network Intrusion Detection System using Machine Learning & Python
Domain Network Security + Artificial Intelligence + SOC Operations
Difficulty (Intermediate–Advanced)
Duration 10–12 weeks
Team Size 1–2 students
Total Cost ₹0 (fully open-source)
Publication Scope GitHub + Research Paper (IJERA / IRJET journals)
Resume Line “Built an ML-based NIDS achieving 97.3% accuracy on KDD Cup 99 dataset; deployed real-time alert dashboard using Streamlit and Scikit-learn”
Tools & Technologies
Category Tools
Language Python 3.x
ML Libraries Scikit-learn, XGBoost, TensorFlow/Keras
Data Processing Pandas, NumPy, Matplotlib, Seaborn
Network Tools Wireshark, Scapy, tcpdump
Dashboard Streamlit / Grafana
Dataset NSL-KDD / CICIDS 2017 / KDD Cup 99 (all free)
Version Control GitHub
Environment Kali Linux VM / Ubuntu
Week-by-Week Execution Plan
Week Task Deliverable
Week 1–2 Dataset selection, EDA, data cleaning, feature engineering Cleaned dataset + EDA report
Week 3–4 Train baseline ML models (Random Forest, SVM, KNN) Model comparison table with accuracy scores
Week 5–6 Implement Deep Learning model (LSTM / CNN for traffic sequences) DL model with improved accuracy
Week 7–8 Build real-time packet capture module using Scapy Live traffic classifier script
Week 9–10 Build Streamlit dashboard with live alerts, traffic graphs, attack logs Working interactive dashboard
Week 11–12 Testing, documentation, GitHub README, write research paper Final project + publishable paper
Deliverables Checklist
Deliverable Format
Trained ML/DL models with accuracy > 95% .pkl / .h5 files
Real-time network traffic classifier Python script
Interactive SOC-style dashboard Streamlit web app
Comparison report — all models vs baselines PDF report
Published GitHub repository with documentation GitHub link
Research paper submitted to journal PDF / DOI link
AI-Powered Network Intrusion Detection System (NIDS)
Project Objective
Build a machine learning-based NIDS that classifies network traffic as normal or malicious in real time, detects attack types (DDoS, port scan, brute force, SQL injection traffic), generates alerts, and produces a visual dashboard — mimicking what enterprise SOC tools like Darktrace and Securonix do.
Project Details Table
Attribute Details
Full Title AI-Powered Network Intrusion Detection System using Machine Learning & Python
Domain Network Security + Artificial Intelligence + SOC Operations
Difficulty (Intermediate–Advanced)
Duration 10–12 weeks
Team Size 1–2 students
Total Cost ₹0 (fully open-source)
Publication Scope GitHub + Research Paper (IJERA / IRJET journals)
Resume Line “Built an ML-based NIDS achieving 97.3% accuracy on KDD Cup 99 dataset; deployed real-time alert dashboard using Streamlit and Scikit-learn”
Tools & Technologies
Category Tools
Language Python 3.x
ML Libraries Scikit-learn, XGBoost, TensorFlow/Keras
Data Processing Pandas, NumPy, Matplotlib, Seaborn
Network Tools Wireshark, Scapy, tcpdump
Dashboard Streamlit / Grafana
Dataset NSL-KDD / CICIDS 2017 / KDD Cup 99 (all free)
Version Control GitHub
Environment Kali Linux VM / Ubuntu
Week-by-Week Execution Plan
Week Task Deliverable
Week 1–2 Dataset selection, EDA, data cleaning, feature engineering Cleaned dataset + EDA report
Week 3–4 Train baseline ML models (Random Forest, SVM, KNN) Model comparison table with accuracy scores
Week 5–6 Implement Deep Learning model (LSTM / CNN for traffic sequences) DL model with improved accuracy
Week 7–8 Build real-time packet capture module using Scapy Live traffic classifier script
Week 9–10 Build Streamlit dashboard with live alerts, traffic graphs, attack logs Working interactive dashboard
Week 11–12 Testing, documentation, GitHub README, write research paper Final project + publishable paper
Deliverables Checklist
Deliverable Format
Trained ML/DL models with accuracy > 95% .pkl / .h5 files
Real-time network traffic classifier Python script
Interactive SOC-style dashboard Streamlit web app
Comparison report — all models vs baselines PDF report
Published GitHub repository with documentation GitHub link
Research paper submitted to journal PDF / DOI link
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