DeepLearning-IDS-master
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：1302KB
下载次数：1
上传日期：2021-04-24 16:19:32
上 传 者：
poppy79
说明： 基于深度学习的网络入侵检测方法，基于python，数据集为KDD
(the method of IDS based on Deep Learing)
文件列表:
LICENSE (1067, 2019-12-15)
createSingleDataFile.py (1346, 2019-12-15)
data_cleanup.py (2835, 2019-12-15)
fastai-expriments.py (4087, 2019-12-15)
fastai_results (0, 2019-12-15)
fastai_results\02-14-2018.results (7399, 2019-12-15)
fastai_results\02-15-2018.results (4504, 2019-12-15)
fastai_results\02-16-2018.results (5560, 2019-12-15)
fastai_results\02-22-2018.results (7071, 2019-12-15)
fastai_results\02-23-2018.results (4551, 2019-12-15)
fastai_results\03-01-2018.results (3458, 2019-12-15)
fastai_results\03-02-2018.results (3492, 2019-12-15)
fastai_results\VMcpu (0, 2019-12-15)
fastai_results\VMcpu\02-14-2018.csv-fastai.out.txt (1940, 2019-12-15)
fastai_results\VMcpu\02-14-2018.csv.result (285, 2019-12-15)
fastai_results\VMcpu\02-15-2018.csv-fastai-out.txt (1949, 2019-12-15)
fastai_results\VMcpu\02-15-2018.csv.result (285, 2019-12-15)
fastai_results\VMcpu\02-16-2018.csv-fastai-out.txt (1939, 2019-12-15)
fastai_results\VMcpu\02-16-2018.csv.result (284, 2019-12-15)
fastai_results\VMcpu\02-22-2018.csv-fastai-out.txt (1942, 2019-12-15)
fastai_results\VMcpu\02-22-2018.csv.result (288, 2019-12-15)
fastai_results\VMcpu\02-23-2018.csv-fastai-out.txt (1944, 2019-12-15)
fastai_results\VMcpu\02-23-2018.csv.result (286, 2019-12-15)
fastai_results\VMcpu\03-01-2018.csv-fastai-out.txt (1942, 2019-12-15)
fastai_results\VMcpu\03-01-2018.csv.result (279, 2019-12-15)
fastai_results\VMcpu\03-02-2018.csv-fastai-out.txt (1944, 2019-12-15)
fastai_results\VMcpu\03-02-2018.csv.result (285, 2019-12-15)
fastai_results\VMcpu\IDS-2018-binaryclass.csv-fastai-out.txt (1942, 2019-12-15)
fastai_results\VMcpu\IDS-2018-binaryclass.csv.result (291, 2019-12-15)
fastai_results\VMcpu\IDS-2018-multiclass.csv-fastai-out.txt (1941, 2019-12-15)
fastai_results\VMcpu\IDS-2018-multiclass.csv.result (291, 2019-12-15)
fastai_results\VMgpu (0, 2019-12-15)
fastai_results\VMgpu\02-14-2018.csv-fastai.out.txt (1936, 2019-12-15)
fastai_results\VMgpu\02-14-2018.csv.result (280, 2019-12-15)
fastai_results\VMgpu\02-15-2018.csv-fastai-out.txt (1949, 2019-12-15)
fastai_results\VMgpu\02-15-2018.csv.result (280, 2019-12-15)
fastai_results\VMgpu\02-16-2018.csv-fastai-out.txt (1924, 2019-12-15)
... ...
# Deep Learning - IDS
Towards Developing a Network Intrusion Detection System using Deep Learning Techniques
- Published article: http://isyou.info/jisis/vol9/no4/jisis-2019-vol9-no4-01.pdf
## Introduction
In this project, we aim to explore the capabilities of various deep-learning frameworks in detecting
and classifying network intursion traffic with an eye towards designing a ML-based intrusion detection system.
## Dataset
- Downloaded from: https://www.unb.ca/cic/datasets/ids-2018.html
- contains: 7 csv preprocessed and labelled files, top feature selected files, original traffic data in pcap format and logs
- used csv preprocessed and labelled files for this research project
## Data Cleanup
- dropped rows with Infinitiy values
- some files had repeated headers; dropped those
- converted timestamp value that was date time format: 15-2-2018 to UNIX epoch since 1/1/1970
- separated data based on attack types for each data file
- ~20K rows were removed as a part of data cleanup
- see data_cleanup.py script for this phase
- \# Samples in table below are total samples left in each dataset after dropping # Dropped rows/samples
## Dataset Summary
| File Name | Traffic Type | # Samples | # Dropped |
| -------------- | ---------------- | --------: | :-------- |
| 02-14-2018.csv | Benign | 663,808 | 3818 |
| | FTP-BruteForce | 193,354 | 6 |
| | SSH-Bruteforce | 187,589 | 0 |
| 02-15-2018.csv | Benign | ***8,050 | 8027 |
| | DOS-GoldenEye | 41,508 | 0 |
| | DOS-Slowloris | 10,990 | 0 |
| 02-16-2018.csv | Benign | 446,772 | 0 |
| | Dos-SlowHTTPTest | 139,890 | 0 |
| | DoS-Hulk | 461,912 | 0 |
| 02-22-2018.csv | Benign | 1,042,603 | 5610 |
| | BruteForce-Web | 249 | 0 |
| | BruteForce-XSS | 79 | 0 |
| | SQL-Injection | 34 | 0 |
| 02-23-2018.csv | Benign | 1,042,301 | 5708 |
| | BruteForce-Web | 362 | 0 |
| | BruteForce-XSS | 151 | 0 |
| | SQL-Injection | 53 | 0 |
| 03-01-2018.csv | Benign | 235,778 | 2259 |
| | Infiltration | 92,403 | 660 |
| 03-02-2018.csv | Benign | 758,334 | 4050 |
| | BotAttack | 286,191 | 0 |
| Traffic Type | # Samples |
| ---------------- | --------: |
| Benign | 5,177,***6 |
| FTP-BruteForce | 193,354 |
| SSH-BruteForce | 187,589 |
| DOS-GoldenEye | 41,508 |
| Dos-Slowloris | 10,990 |
| Dos-SlowHTTPTest | 139,890 |
| Dos-Hulk | 461,912 |
| BruteForce-Web | 611 |
| BruteForce-XSS | 230 |
| SQL-Injection | 87 |
| Infiltration | 92,403 |
| BotAttack | 286,191 |
| Total Attack | 1,414,765 |
## Deep Learning Frameworks
- perfomance results using various deep learning frameworks are compared
- 10-fold cross-validation techniques was used to validate the model
### FastAI
- https://www.fast.ai/
- uses PyTorch, https://pytorch.org/ as the backend
### Keras
- https://keras.io/
- using TensorFlow and Theano as backend
- https://www.TensorFlow.org/
- https://github.com/Theano/Theano
## Experiment Results
### Using Salamander.ai
| Dataset | Framework | Accuracy (%) | Std-Dev | GPU Time (~mins) |
| ----------- | ----------------- | -----------: | ------: | ---------------: |
| 02-14-2018 | FastAI | 99.85 | 0.07 | \* |
| | Keras-TensorFlow | ***.80 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 02-15-2018 | FastAI | 99.*** | 0.01 | 25 |
| | Keras-Tensorfflow | 99.32 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 02-16-2018 | FastAI | 100.00 | 0.00 | 16 |
| | Keras-TensorFlow | 99.84 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 02-22-2018 | FastAI | 99.87 | 0.15 | 110 |
| | Keras-TensorFlow | 99.97 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 02-23-2018 | FastAI | 99.92 | 0.00 | 120 |
| | Keras-TensorFlow | 99.94 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 03-01-2018 | FastAI | 87.00 | 0.00 | 5 |
| | Keras-TensorFlow | 72.20 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| 03-02-2018 | FastAI | 99.97 | .01 | 75 |
| | Keras-TensorFlow | ***.12 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| === | === | === | === | === |
| Multiclass | Keras-TensorFlow | 94.73 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| | FastAI | \* | \* | \* |
| Binaryclass | Keras-TensorFlow | 94.40 | \* | \* |
| | Keras-Theano | \* | \* | \* |
| | FastAI | \* | \* | \* |
### FastAI Results
#### Summary Results
| Data File | Accuracy | Loss |
| -------------------------- | -------: | ------: |
| 02-14-2018.csv | 99.99% | 0.00212 |
| 02-15-2018.csv | 99.86% | 0.02500 |
| 02-16-2018.csv | 99.97% | 324160 |
| 02-22-2018.csv | 99.97% | 0.00221 |
| 02-23-2018.csv | 99.82% | 0.06295 |
| 03-01-2018.csv | 87.14% | 0.37611 |
| 03-02-2018.csv | 99.72% | 0.85127 |
| IDS-2018-binaryclass.csv\* | ***.68% | 0.37692 |
| IDS-2018-multiclass.csv\* | ***.31% | 7.06169 |
\* Trained on VMgpu
#### Confusion Matrices
| 02-14-2018 | 02-15-2018 | 02-16-2018 |
| :----------------------------------------------------------: | :----------------------------------------------------------------: | :---------------------------------------------------------------: |
| .png>) | .png>) | .png>) |
| 02-22-2018 | 02-23-2018 | 03-01-2018 |
| .png>) | .png>) | .png>) |
| 03-02-2018 | IDS-2018-binaryclass | IDS-2018-multiclass |
| .png>) |  |  |
### Attack Sample Distribution and Detection Accuracy
| Data File | % of Attack Samples | % Attacks Flagged Correctly | % Benign Flagged Incorrectly |
| ------------- | ------------------: | --------------------------: | ---------------------------: |
| 02-14-2018 | 3***6 | 100.00 | 0.00\* |
| 02-15-2018 | 5.04 | 99.85 | 0.00\* |
| 02-16-2018 | 57.39 | 100.00 | 0.00\* |
| 02-22-2018 | 0.00\* | 0.02 | 0.00 |
| 02-23-2018 | 0.00\* | 61.61 | 0.00\* |
| 03-01-2018 | 28.16 | 73.19 | 10.16 |
| 03-02-2018 | 27.40 | 99.85 | 0.00\* |
| Binary-Class | 21.50 | 94.60 | 0.21 |
| Multi-Class | 21.50 | 93.9 | 0.48 |
\* Small, non-zero values
### Using VMgpu
| Dataset | Framework | Accuracy (%) | Std-Dev | GPU Time (~mins) |
| ----------- | ---------------- | :----------: | :-----: | :--------------: |
| 02-14-2018 | FastAI | 99.54 | 0.32 | 100.36 |
| | Keras-TensorFlow | 99.14 | \* | 100.29 |
| | Keras-Theano | ***.58 | \* | \* |
| 02-15-2018 | FastAI | 99.*** | 0.01 | 103.16 |
| | Keras-TensorFlow | 99.33 | \* | 96.34 |
| | Keras-Theano | 99.17 | \* | \* |
| 02-16-2018 | FastAI | 99.66 | 0.25 | 104.51 |
| | Keras-TensorFlow | 99.66 | \* | 99.59 |
| | Keras-Theano | 99.41 | \* | \* |
| 02-22-2018 | FastAI | 99.90 | 0.09 | 102.83 |
| | Keras-TensorFlow | 99.97 | \* | 96.71 |
| | Keras-Theano | 99.97 | \* | \* |
| 02-23-2018 | FastAI | 99.88 | 0.08 | 104.43 |
| | Keras-TensorFlow | 95.95 | \* | 100.79 |
| | Keras-Theano | 99.95 | \* | \* |
| 03-01-2018 | FastAI | 8***7 | 0.78 | 33.23 |
| | Keras-TensorFlow | 72.16 | \* | 33.15 |
| | Keras-Theano | 72.04 | \* | \* |
| 03-02-2018 | FastAI | 99.94 | 0.04 | 104.34 |
| | Keras-TensorFlow | ***.47 | \* | 105.95 |
| | Keras-Theano | 93.95 | \* | \* |
| === | === | === | === | === |
| Multiclass | FastAI | ***.60 | 0.16 | 683.12 |
| | Keras-TensorFlow | 92.09 | \* | 652.89 |
| | Keras-Theano | \* | \* | \* |
| Binaryclass | FastAI | ***.75 | 0.05 | 675.*** |
| | Keras-TensorFlow | 95.14 | \* | 632.36 |
| | Keras-Theano | \* | \* | \* |
### fastai CPU vs GPU training time on vmGPU
| Dataset | Hardware | Accuracy (%) | Time (~mins) |
| ----------- | -------- | :----------: | :----------: |
| 02-14-2018 | | | |
| | CPU | 99.86 | 1193.84 |
| | GPU | 99.54 | 100.36 |
| 02-15-2018 | | | |
| | CPU | 99.93 | 1299.55 |
| | GPU | 99.89 | 103.16 |
| 02-16-2018 | | | |
| | CPU | 99.88 | 433.63 |
| | GPU | 99.66 | 104.51 |
| 02-22-2018 | | | |
| | CPU | 99.83 | 3091.34 |
| | GPU | 99.90 | 102.83 |
| 02-23-2018 | | | |
| | CPU | 99.83 | 1938.74 |
| | GPU | 99.88 | 104.43 |
| 03-01-2018 | | | |
| | CPU | 85.39 | 80.07 |
| | GPU | 8***7 | 33.23 |
| 03-02-2018 | | | |
| | CPU | 99.76 | 1503.18 |
| | GPU | 99.94 | 104.34 |
| === | === | === | === |
| Multiclass | | | |
| | CPU | 96.63 | 19361.95 |
| | GPU | ***.60 | 683.12 |
| Binaryclass | | | |
| | CPU | 96.66 | 19441.55 |
| | GPU | ***.75 | 632.36 |
# References
1. Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018
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