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QCF for deep packet inspection论文总结
2022-07-26 14:43:00 【桐青冰蝶Kiyotaka】
QCF for deep packet inspection论文总结
Abstract

作者提出了一种新的成员查询数据结构变体,称为基于商的 Cuckoo 过滤器 (QCF),它反映了 QF 和 CF 之间的合并过程。
Introduction
- QCF use only two hash functions rather than three in the CF.
- Consuming the same memory requirements just such as CF.
- Employing only two hash functions rather than three in CF making it faster than BF, QF, and CF.
- Having same false-positive rate of CF which is smaller than the false-positive rate of BF and QF making it more accurate.
- Simpler to implement and consume less computational complexity than BF, QF, and CF.
- Supports delete operation.
- The ability to make dynamic signatures update and doing that quickly.
- Higher throughput than using BF, QF, and CF.
Related works

- 近似成员查询数据结构而不是使用有限自动机进行正则表达式匹配
- 实现简单、节省内存、对进行动态更新的快速响应、无需特定自动机
Deep PI
DPI 代表了 NIDS 通过检查网络流量内容来发现攻击和异常的主要任务。
区分良好 DPI 系统的主要两个特征:
- 执行强签名检测的能力(the ability to perform strong signature detection)
- 以高吞吐量和最小延迟生成结果的能力(produce the results with high throughput and minimum latency)
Probabilistic data structures
Bloom filter

false-positive rate f:
Quotient filter

详细介绍见这篇文章: Fast 2D filter with low false positive for network packet inspection论文总结
Cuckoo filter

Insertion process

Lookup process

Deletion process

Proposed QCF


- 第一个哈希函数中检索到的值被用作第一个候选桶的索引
- 第一个哈希函数检索到的值应用商法,以获得用作元素指纹的剩余部分
- 第二个哈希函数用于对元素的指纹进行哈希处理,获得第二个候选桶

Main steps of retrieving element’s fingerprint and the two candidate buckets in QCF:
Main steps of retrieving element’s fingerprint and the two candidate buckets in QCF and an example for QCF that has four buckets (three entries per bucket)
- 元素x将被发送到第一个哈希函数hash1,返回的值将用作第一个候选桶的索引
- 同时,对该返回值应用一个商技术,以从r最低有效位中检索X的指纹f
- 异或计算第二个候选存储桶的索引
- 指纹f将被插入到这两个候选桶之一
Insertion process of QCF

计算两个可能的候选桶的索引。接下来,根据计算的指纹f,检查这两个可能的候选桶,如果其中任何一个持有这个指纹,查找/删除
Query process of QCF

Deletion process of QCF

Proposed DPI system architecture
所有给定的(即预定义的)签名都被映射到所使用的 QCF 中,以便稍后用作监控传入流量的识别工具。
系统通过监控传入流量并根据已映射到 QCF 的签名检查每个到达的数据包来工作

Evaluation
Insertion throughput
QCF 比其他过滤器具有更高的插入吞吐量,这是减少计算开销的结果。
Query throughput
QCF 消耗的计算量更少,因此它比 CF、BF 和 QF 具有更高的查询吞吐量。
Deletion throughput
与 QF 相反,CF 和 QCF 提供稳定的性能,因为它们的删除是独立于过滤器占用的。
DPI time improvement
与使用 CF 相比,平均时间改进高达 77%,与使用 BF 和 QF 相比,平均时间改进高达 98%。
Conclusion

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