| Enhanced Aljubail Scenario | Urban Attack Scenario |
|---|---|
 |
 |
VeReMiVNDN is a comprehensive simulation framework and dataset generator for Intrusion Detection Systems (IDS) in Vehicular Named Data Networks (VNDN). This project implements 20 different multi-layer attacks and provides rich feature extraction for machine learning-based security research.
Dataset and simulation results are available in the GitHub Releases section.
- Complete NDN Protocol Stack for vehicular networks
- 20 Attack Types across all network layers
- Rich Feature Set: Trust, Privacy, Temporal, Mobility, Cache, Network metrics
- ML-Ready Datasets: CSV, JSON formats with ground truth labels
- Multi-Layer Detection: Network, Data, Caching, Privacy, Trust, Cross-layer
- Realistic Scenarios: Integration with SUMO, VEINS, INET 4.5, Simu5G
- Interest Flooding - PIT exhaustion attack
- Name Prefix Hijacking - Route hijacking
- Interest Aggregation Attack - Resource imbalance
- Routing Information Flood - FIB/PIT spoofing
- Content Poisoning - Fake data injection
- Interest/Content Replay - Stale content attacks
- Cache Invalidation - Malicious cache churn
- Producer Impersonation - Fake producer attacks
- Cache Pollution - Unpopular content flooding
- Cache Partitioning - Availability manipulation
- Content Privacy Leakage - Cache sharing attacks
- Cache Timing Attacks - Timing-based inference
- Privacy De-anonymization - Name semantics exploitation
- Name Enumeration - Privacy crawling
- Sybil Content Amplification - Multiple fake identities
- Collusion - Coordinated misbehavior
- Signature Forgery - Key compromise attacks
- Selective Forwarding - Gray hole attacks
- Radio Jamming - PHY layer interference
- Machine Learning Evasion - Adversarial examples
┌─────────────────────────────────────────────────────────────┐
│ SUMO Traffic Simulator │
│ (Realistic Vehicle Mobility) │
└────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ VEINS (Vehicle Communication) │
│ INET 4.5 + Simu5G (Network Stack) │
└────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ VNDN Protocol Layer │
│ ┌──────────────┬──────────────┬────────────────────────┐ │
│ │ Interest │ Data │ NACK │ │
│ └──────────────┴──────────────┴────────────────────────┘ │
│ ┌──────────────┬──────────────┬────────────────────────┐ │
│ │ PIT │ FIB │ Content Store (CS) │ │
│ └──────────────┴──────────────┴────────────────────────┘ │
└────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Attack Modules (20 Types) │
│ Network │ Data │ Cache │ Privacy │ Trust │ Cross-Layer │
└────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ IDS Detection & Data Collection │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Feature Extraction: Network, Temporal, Trust, │ │
│ │ Cache, Mobility, Privacy Metrics │ │
│ └────────────────────────────────────────────────────┘ │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Logging: CSV, JSON + Ground Truth Labels │ │
│ └────────────────────────────────────────────────────┘ │
└────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ VeReMiVNDN Dataset │
│ Train / Test / Validation Sets with Labels │
└─────────────────────────────────────────────────────────────┘
- OMNeT++ 6.0.3
- VEINS 5.3
- INET 4.5
- Simu5G
- SUMO (latest version)
- Python 3.8+ (for data processing)
git clone https://github.com/Muhammaduazir69/VeReMiVNDN-dataset.git
cd VNDN-Security
# Build the project
make makefiles
make -j$(nproc)cd simulations
opp_run -u Cmdenv -c BasicVNDN -n ..:../src omnetpp.iniopp_run -u Cmdenv -c AttackScenario_IF -n ..:../src omnetpp.inipython3 utils/parsers/parse_omnet_results.py --input simulations/results --output datasets/processedThe full simulation dataset (results, frames, and OMNeT++ logs) is available in the Releases section:
| File | Description |
|---|---|
VeReMiVNDN-OmNetpp-Dataset.zip |
Raw OMNeT++ simulation dataset |
VeReMiVNDN-Dataset-Results.zip |
Processed simulation results |
VeReMiVNDN-Frames.zip |
Simulation frame captures |
- Network: RSSI, delay, throughput, packet loss, jitter
- NDN: PIT size, cache hit ratio, interest satisfaction rate, hop count
- Trust & Security: node trust score, content trust score, anomaly score
- Temporal: inter-packet arrival time, burst patterns, attack duration
- Mobility: speed, acceleration, position, direction, RSU distance
- Privacy: name entropy, cache access patterns, timing variance
timestamp,node_id,attack_type,attack_active,severity,layer
1.234,vehicle[5],InterestFlooding,1,0.85,Network
2.456,vehicle[12],Benign,0,0.0,NoneMain config: simulations/configs/omnetpp.ini
[General]
network = VndnNetwork
sim-time-limit = 300s
*.numVehicles = 100
*.numRSU = 5
*.numMalicious = 10
*.vehicle[*].attackType = "InterestFlooding"
*.**.ids.enabled = true| Feature | VeReMi | F2MD | MisbehaviorX | VeReMiVNDN |
|---|---|---|---|---|
| Protocol | VANET | VANET | V2X | VNDN |
| Attack Types | 5 | 8 | 68 | 20 (VNDN-specific) |
| Trust Scores | ✗ | ✗ | ✗ | ✓ |
| Privacy Features | ✗ | ✗ | ✗ | ✓ |
| Cache Metrics | ✗ | ✗ | ✗ | ✓ |
| Multi-Layer | ✗ | ✗ | Limited | ✓ |
| NDN Support | ✗ | ✗ | ✗ | ✓ |
If you use this framework or dataset in your research, please cite:
@misc{veremivndn2025,
title={VeReMiVNDN: A Comprehensive Dataset for Intrusion Detection in Vehicular Named Data Networks},
author={Muhammad Uzair},
year={2025},
publisher={GitHub},
howpublished={\url{https://github.com/Muhammaduazir69/VeReMiVNDN-dataset}}
}This project is licensed under the MIT License - see LICENSE file.
- VeReMi Dataset project
- F2MD Framework
- OMNeT++ Community
- VEINS and INET Development Teams
- GitHub Issues: https://github.com/Muhammaduazir69/VeReMiVNDN-dataset/issues
Version: 1.0.0 | Status: Active