mixeway/vulnauditor

Mixeway Vuln Auditor

Mixeway Vuln Auditor是一款用于以下目的的应用：

1. 为多种机器学习算法创建测试套件，验证构建高效软件漏洞分类器的可能性
2. 提供API以预测漏洞类别（CRV、DNRV）

目录说明

• model：包含已保存的模型和分词器字典，供REST API使用
• plot：包含生成的测试套件相关图表
• src：包含原型源代码

测试套件

Vuln Auditor实现了针对神经网络、随机森林和支持向量机的测试套件。

要求

1. /data/目录需包含CSV文件，表头为("app_name","app_context","vuln_name","vuln_desc","severity","grade")，未提供训练和测试数据
2. TensorFlow 2.0+
3. Python 3

使用方式

shell
py ./src/main/MixewayVulnAuditor.py

示例输出：

shell
Loading file:  C:\Users\gsiew\IdeaProjects\MixewayVulnAuditor\data\audit_code.csv
Loading file:  C:\Users\gsiew\IdeaProjects\MixewayVulnAuditor\data\audit_infra.csv
Loading file:  C:\Users\gsiew\IdeaProjects\MixewayVulnAuditor\data\audit_os.csv
Loading file:  C:\Users\gsiew\IdeaProjects\MixewayVulnAuditor\data\audit_webapp.csv
Prepared dictionary for tokenizer
Train set size:  37737
Train set Labels:  {0: 24254, 1: 13483}
Test set size:  16173
Test set Labels:  {0: 10384, 1: 5789}
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, 863, 32)           607264    
_________________________________________________________________
flatten (Flatten)            (None, 27616)             0         
_________________________________________________________________
dense (Dense)                (None, 32)                883744    
_________________________________________________________________
dense_1 (Dense)              (None, 32)                1056      
_________________________________________________________________
dense_2 (Dense)              (None, 16)                528       
_________________________________________________________________
dense_3 (Dense)              (None, 16)                272       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 17        
=================================================================
Total params: 1,492,881
Trainable params: 1,492,881
Non-trainable params: 0
_________________________________________________________________
Layers:  7
Epoch 1/50
1180/1180 [==============================] - 17s 14ms/step - loss: 0.2768 - accuracy: 0.9699 - f1: 0.9494 - precision: 0.9511 - recall: 0.9554 - val_loss: 0.1249 - val_accuracy: 0.9850 - val_f1: 0.9779 - val_precision: 0.9644 - val_recall: 0.9940
Epoch 2/50

模型指标图表将存储在/plot/目录中

REST API

要求：

• SSL证书和密钥需存放在环境变量指定的位置（CERTIFICATE和PRIVATEKEY）
• model目录需包含模型描述，若为空则会使用data目录中的测试数据创建模型

使用方式

shell
py ./src/main/vuln_auditor_server.py

REST API接口

方法 POST http://localhost:8445/vuln/predict

示例请求体：

json
[
	{
		"id": 1,
		"appName": "Apollo",
		"appContext": "type opensource customer internal",
		"vulnName": "CVE-2016-1000339",
		"vulnDescription": "In the Bouncy Castle JCE Provider version 1.55 and earlier the primary engine class used for AES was AESFastEngine. Due to the highly table driven approach used in the algorithm it turns out that if the data channel on the CPU can be monitored the lookup table accesses are sufficient to leak information on the AES key being used. There was also a leak in AESEngine although it was substantially less. XXEOS has been modified to remove any signs of leakage (testing carried out on Intel X86-64) and is now the primary AES class for the BC JCE provider from 1.56. Use of AESFastEngine is now only recommended where otherwise deemed appropriate.",
		"severity": "Medium"
	}
]

示例响应：

json
[
  {
    "id": 1,
    "audit": 0
  }
]