Theory & Background

NSL-KDD has 41 features per connection record with labels: Normal, DoS (Denial of Service), Probe (network scanning), R2L (Remote-to-Local), U2R (User-to-Root). Handle class imbalance: SMOTE oversampling for minority attack classes. Feature selection: select top 20 features by Random Forest importance to reduce dimensionality. Train XGBoost: achieves 99.2% accuracy on NSL-KDD. More challenging: CIC-IDS2018 dataset with modern attack types (web attacks, DDoS, infiltration).

Components & Requirements

10 components required for this project.

#	Component	Purpose	Qty
1	Python 3.10+	IDS implementation	x1
2	Scapy	Live packet capture and parsing	x1
3	scikit-learn	ML classifiers (Random Forest, SVM)	x1
4	XGBoost	Gradient boosting for attack classification	x1
5	NSL-KDD dataset	Labeled attack traffic training data	x1
6	Wireshark/tshark	Reference packet analysis	x1
7	Kafka	Real-time packet stream processing	x1
8	Elasticsearch + Kibana	Alert storage and visualization	x1
9	PCAP files (CICIDS2018)	Modern attack dataset	x1
10	Flask	IDS dashboard and alert API	x1

Step-by-Step Implementation

Follow these 2 steps carefully.

1

Model Training on NSL-KDD

NSL-KDD has 41 features per connection record with labels: Normal, DoS (Denial of Service), Probe (network scanning), R2L (Remote-to-Local), U2R (User-to-Root). Handle class imbalance: SMOTE oversampling for minority attack classes. Feature selection: select top 20 features by Random Forest importance to reduce dimensionality. Train XGBoost: achieves 99.2% accuracy on NSL-KDD. More challenging: CIC-IDS2018 dataset with modern attack types (web attacks, DDoS, infiltration).

2

Real-Time Detection Pipeline

Scapy sniffs network interface: sniff(iface=

Code & Implementation

Core code for ids.py:

ids.py
  Python

Testing & Troubleshooting

Test Network Intrusion Detection System by verifying each subsystem individually before full integration.

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Troubleshooting Tips

Verify power voltages, check ground connections, use serial monitor for debug.

Real-World Applications

*Enterprise network perimeter defense

*Data center east-west traffic monitoring

*Industrial control system network security

*Cloud workload protection platform

*Critical infrastructure cybersecurity

*Academic research network monitoring

*Telecommunications network security

*Financial institution threat detection

Extensions & Next Steps

Add MITRE ATT&CK framework mapping to detected attacks
Implement adversarial attack detection (adversarial ML attacks on the IDS)
Build automated IP blocking via firewall API integration
Add honeypot integration for deception-based threat intelligence
Implement federated IDS sharing threat indicators between organizations

Interactive Playground

Coming Soon

An interactive simulator will be available here — simulate circuits and run code in-browser without hardware.

Frequently Asked Questions

What is the difference between signature-based and anomaly-based IDS?

Signature-based IDS maintains a database of known attack patterns (network packet signatures, byte sequences). Excellent at detecting known attacks with near-zero false positives. Cannot detect novel (zero-day) attacks. Example: Snort/Suricata with rule databases. Anomaly-based IDS establishes a baseline of normal behavior, flags significant deviations. Can detect novel attacks but suffers from false positives (legitimate but unusual traffic flagged). ML-based IDS as in this project is anomaly-based. Production systems use both in tandem.

Introduction