File size: 3,063 Bytes
fe52f9b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
## README.md (with Metadata)
```yaml
---
model_name: "Canstralian/CySec_Known_Exploit_Analyzer"
tags:
  - cybersecurity
  - exploit-detection
  - network-security
  - machine-learning
license: mit
datasets:
  - "cysec-known-exploit-dataset"
metrics:
  - accuracy
  - f1
  - precision
  - recall
library_name: transformers
language:
  - en
model_type: neural-network
---
```

# Canstralian/CySec_Known_Exploit_Analyzer

## Overview
The **CySec Known Exploit Analyzer** is a model designed to detect and analyze known cybersecurity exploits. This model was built to assist in identifying vulnerabilities and exploit attempts in network traffic by leveraging machine learning algorithms. It is designed for real-time detection and analysis of potential threats.

## Model Details
- **Type:** Neural Network
- **Input:** Network traffic logs, exploit payloads, or relevant security data
- **Output:** Classification of known exploits, anomaly detection
- **Training Data:** Trained on the **cysec-known-exploit-dataset**, which includes real-world exploit samples and traffic data.
- **Architecture:** Custom Neural Network with attention layers for detecting exploit signatures in packet data.
- **Metrics:** The model was evaluated using accuracy, F1 score, precision, and recall to measure its performance.

## Getting Started

### Installation
To clone the repository and install necessary dependencies:
```bash
git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
cd CySec_Known_Exploit_Analyzer
pip install -r requirements.txt
```

### Usage
To analyze a network traffic log:
```bash
python analyze_exploit.py --input [input-file]
```

### Example
```bash
# Example command to analyze a sample log
python analyze_exploit.py --input data/sample_log.csv
```

### Model Inference
- **Input:** Network traffic logs in CSV format
- **Output:** Classification of potential exploits with confidence scores

## License
This project is licensed under the MIT License. See the LICENSE.md file for more details.

## Datasets
The model was trained using the **cysec-known-exploit-dataset**, which consists of exploit data collected from real-world network traffic.

## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## Contact
For any questions or feedback, feel free to open an issue or reach out to [Your Email].

---

### **Metadata Fields Explained:**
- **`model_name`:** The name of the model.
- **`tags`:** Keywords for discoverability.
- **`license`:** Chosen license (MIT).
- **`datasets`:** Dataset used for training.
- **`metrics`:** Metrics for model evaluation.
- **`library_name`:** Library used to build the model.
- **`language`:** Language of the documentation.
- **`model_type`:** Type of model.

This structure will enhance discoverability and usability for users on platforms like Hugging Face or GitHub. If you need any more adjustments or additions, let me know!

## Commit Options
- Commit directly to the **main** branch.
- Open as a pull request to the **main** branch.
```