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5 | 5 | This project presents a complete pipeline for real-time detection and classification of Distributed Denial of Service (DDoS) attacks. It leverages deep learning techniques, combining an LSTM Autoencoder for anomaly detection and a DNN for multi-class attack classification. The solution is based on realistic traffic data and supports real-time deployment scenarios. |
6 | 6 |
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7 | 7 |  |
8 | | - |
| 8 | +<!-- <img src = "Assets/System_outline.png" width=00> --> |
9 | 9 | --- |
10 | 10 |
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11 | 11 | ## Key Features |
@@ -60,12 +60,39 @@ The dashboard visualizes network anomaly detection results. It showcases distrib |
60 | 60 |
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61 | 61 | --- |
62 | 62 |
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63 | | -## How to Run |
| 63 | +## Project setup |
64 | 64 |
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65 | 65 | 1. Clone the repository: |
66 | 66 | ```bash |
67 | 67 | git clone https://github.com/yourusername/multiclass-ddos-detector.git |
68 | 68 | cd multiclass-ddos-detector |
69 | 69 |
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| 70 | +2. Move Files to Respective Virtual Machines (VMs) |
| 71 | + |
| 72 | +3. Ensure that Parrot os and target machine/s are on the same network |
| 73 | + |
| 74 | + |
| 75 | +## How to run |
| 76 | + |
| 77 | +1. Execute the attck script from Parrot OS |
| 78 | + ```bash |
| 79 | + sudo python3 DDoS_sim.py -i <low/medium/high> -s -p <target port> <target IP> -d <attack_duration> |
| 80 | + |
| 81 | +2. Run CICFlowMeter on Ubuntu |
| 82 | + ```bash |
| 83 | + sudo bash run_cicflowmeter.sh |
| 84 | + |
| 85 | +3. Send the generated CSV file to the host machine |
| 86 | + ```bash |
| 87 | + sudo bash send_flow_file.sh |
| 88 | + |
| 89 | +- choose the file |
| 90 | +- enter IP address of host |
| 91 | + |
| 92 | +4. Receive File → Run Detection Model → Launch Dashboard |
| 93 | + ```bash |
| 94 | + python .\ddos_manager.py |
| 95 | + |
| 96 | + |
70 | 97 |
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71 | 98 |
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