SDN架构下的安全审计系统研究与实现

刘静; 何运; 赖英旭

doi:10.11936/bjutxb2016050070

SDN架构下的安全审计系统研究与实现

doi: 10.11936/bjutxb2016050070

1.
北京工业大学计算机学院, 北京 100124
2.
可信计算北京市重点实验室, 北京 100124
3.
信息安全等级保护关键技术国家工程实验室, 北京 100124

基金项目: 北京市自然科学基金资助项目(4162006)

详细信息

中图分类号: TP393.0
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- 被引次数: 0
出版历程
- 收稿日期: 2016-05-29
- 网络出版日期: 2022-09-13
- 刊出日期: 2017-02-01

Research and Implementation of Security Audit on SDN Architecture

1.
College of Computer Science, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Trusted Computing, Beijing 100124, China
3.
National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China

摘要

摘要: 为了解决软件定义网络(software defined networking, SDN)架构面临的安全挑战,针对SDN网络架构中的安全审计环节,将传统网络中的安全审计解决方案与SDN网络集中控制的特性相结合,依托Floodlight控制器设计并实现适用于SDN网络环境的安全审计系统,包括安全审计事件的收集、分析、存储、响应等功能. 提出一种针对分布式拒绝服务(distributed denial of service, DDoS)攻击的攻击回溯算法对安全审计事件进行追溯,确定出DDoS攻击发起者及僵尸主机集合. 同时,采用滑动窗口分割算法从安全审计事件中提取出用户行为序列模式,基于Levenshtein算法计算用户行为序列模式之间的相似度,并根据用户当前行为和历史行为的相似度来判断是否出现可疑的攻击行为. 经实验验证,该系统能准确地回溯出DDoS攻击发生时被控的僵尸主机集合及攻击者,并且可以有效地检测出用户攻击行为.
- 软件定义网络(SDN) /
- 安全审计 /
- Floodlight /
- 攻击回溯 /
- 用户行为分析
Abstract: To address security challenges in software defined networking (SDN) architecture, centered on the security audit aspect of the SDN architecture, the traditional network security audit solutions and the SDN architecture’s centralized control features were combined. A security audit system was designed and implemented based on the Floodlight controller and was operated in the SDN environment, in which the collection, analysis, storage of audit events and other functions were included. A backtracking algorithm against DDoS scenario was designed to detect the attackers and dummy hosts via reviewing and analyzing security audit events retrospectively. Besides, a sliding window segmentation algorithm was proposed which extracted user’s behavior patterns after implementing sequence analysis against security audit events. Based on the Levenshtein algorithm to the similarity of sequence patterns were calculated, then according to the similarity of the current user’s behaviors and historical behaviors, suspected attack behaviors were detected.
- software defined networking (SDN) /
- security audit /
- Floodlight /
- attack backtracking /
- user behavior analysis
The authors have declared that no competing interests exist.

HTML全文

图 1 DDoS攻击时序图

Figure 1. Attack sequence diagram

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图 2 用户行为模式挖掘原理

Figure 2. User behavior patterns mining

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图 3 系统总体框架图

Figure 3. System framework diagram

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图 4 事件生成模块工作原理图

Figure 4. Event generator module principle diagram

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图 5 事件分析模块工作原理图

Figure 5. Event analysis module principle diagram

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图 6 自动响应模块工作原理图

Figure 6. Automatic response module principle diagram

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图 7 自动响应模块引流功能示意图

Figure 7. Automatic response module collect flow function diagram

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图 8 实验拓扑

Figure 8. Experiment topology

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图 9 源自PC1的UDP泛洪数据包

Figure 9. UDP packet flooding from PC1

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图 10 源自PC2的UDP泛洪数据包

Figure 10. UDP packet flooding from PC2

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图 11 源自PC3的UDP泛洪数据包

Figure 11. UDP packet flooding from PC3

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图 12 算法执行结果

Figure 12. Results of the algorithm

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表 1 主机角色及IP地址分配表

Table 1. Host role and IP address allocation table

主机角色	表头
Attacker	172.16.10.10
Zombie Hosts PC1	172.16.10.11
Zombie Hosts PC2	172.16.10.13
Zombie Hosts PC3	172.16.10.14
Target Host	172.16.10.88

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表 2 参数Δt_attack对僵尸主机集合ZHset和攻击者Attackers以及迭代次数i的影响

Table 2. Influence of parameter Δt_attack change on ZHset, Attackers and i

Δt_attack/s	5	6	7	8	9	10
ZHset	172.16.10.13	172.16.10.13172.16.10.14	172.16.10.13172.16.10.14	172.16.10.13172.16.10.14172.16.10.11	172.16.10.13172.16.10.14172.16.10.11	172.16.10.13172.16.10.14172.16.10.11
Attackers	172.16.10.10	172.16.10.10	172.16.10.10	172.16.10.10	172.16.10.10	172.16.10.10
i	0	0	0	0	0	0
Δt_attack/s	11	12	13	14	15
ZHset	172.16.10.13	172.16.10.13172.16.10.14	172.16.10.13172.16.10.14	172.16.10.13172.16.10.14172.16.10.11	172.16.10.13172.16.10.14172.16.10.11
Attackers	172.16.10.10	172.16.10.10172.16.10.88	172.16.10.10172.16.10.88	172.16.10.10172.16.10.88	172.16.10.10172.16.10.88
i	0	8	8	8	8

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表 3 172.16.10.10在不同支持度下的序列模式示例

Table 3. Sequence pattern examples of 172.16.10.10 under different support

最小支持度/%	序列模式数	模式示例	说明
10	4	[‘172.16.10.14’]→[‘172.16.10.11’]→[‘172.16.10.13’]	该用户经常连续访问172.16.10.14、172.16.10.11、172.16.10.13,且访问顺序基本保持不变
5	16	[‘172.16.10.14’]→[‘172.16.10.11’]→[‘172.16.10.13’]→[‘172.16.10.12’]	该用户经常连续访问172.16.10.14、172.16.10.11、172.16.10.13、172.16.10.12,且访问顺序基本保持不变
3	29	[‘172.16.10.14’]→[‘172.16.10.11’]→[‘172.16.10.13’]→[‘172.16.10.12’]→[‘172.16.10.14’]	该用户经常连续访问172.16.10.14、172.16.10.11、172.16.10.13、172.16.10.12,且访问顺序总是14→11→13→12,支持度降低时,可获得相对较长序列模式

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表 4 3个僵尸主机用户行为序列模式分析结果

Table 4. Analysis of three bots user behavior sequential pattern

用户	支持度/%	序列模式	说明
172.16.10.11	80	[‘172.16.10.88’]→[‘172.16.10.88’]→[‘172.16.10.88’]→[‘172.16.10.88’]	对1d内的网络访问记录进行分析,在支持度为80%的基础上出现长度为4且连续访问同一个IP的序列模式,该序列模式可视为具有潜在攻击行为的序列模式
172.16.10.13	80
172.16.10.14	80

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表 5 基于Levenshtein算法序列相似度的计算结果

Table 5. Sequence similarity calculation results based on the Levenshtein algorithm

历史行为序列模式	当前行为序列模式	相似度	说明
[‘172.16.10.14’]→[‘172.16.10.11’]→[‘172.16.10.13’]	[‘172.16.10.14’]→[‘172.16.10.11’]→[‘172.16.10.12’]	0.67	对用户172.16.10.10而言,当其新的行为模式和历史具有安全威胁的行为模式的相似度达到一个阈值(自定义)时,可以判断此时该用户的网络访问行为可能为攻击行为
[‘172.16.10.88’]→[‘172.16.10.88’]→[‘172.16.10.88’]	[‘172.16.10.88’]→[‘172.16.10.88’]→[‘172.16.10.88’]	1.00	对一般用户而言,当在某个时间段内(如上文)出现连续不断地访问某个目标IP的行为序列时,可以判断此行为可能为攻击行为,并发出警报

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