网络与信息安全学报 ›› 2021, Vol. 7 ›› Issue (4): 114-130.doi: 10.11959/j.issn.2096-109x.2021050
李玎1,2, 祝跃飞1,2, 芦斌1,2, 林伟1,2
修回日期:
2020-09-24
出版日期:
2021-08-15
发布日期:
2021-08-01
作者简介:
李玎(1992− ),男,河南郑州人,信息工程大学博士生,主要研究方向为网络信息安全、机器学习基金资助:
Ding LI1,2, Yuefei ZHU1,2, Bin LU1,2, Wei LIN1,2
Revised:
2020-09-24
Online:
2021-08-15
Published:
2021-08-01
Supported by:
摘要:
网络加密流量侧信道攻击通过分析、提取网络应用通信过程中泄露的数据包长度、时间等侧信道信息,能够识别用户的身份和行为,甚至还原用户输入的原始数据。基于信息论建立了网络加密流量侧信道攻击模型,使用统一的模型框架分析了代表性的指纹攻击、击键攻击和语音攻击的方法和效果,讨论了基于隐藏数据包长度和时间信息的防御方法,结合技术发展前沿对未来可能的研究方向进行了展望。
中图分类号:
李玎, 祝跃飞, 芦斌, 林伟. 网络加密流量侧信道攻击研究综述[J]. 网络与信息安全学报, 2021, 7(4): 114-130.
Ding LI, Yuefei ZHU, Bin LU, Wei LIN. Survey of side channel attack on encrypted network traffic[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 114-130.
表1
网站指纹攻击方法比较Table 1 Comparison of website fingerprinting attack methods"
作者 | 隧道类型 | 模型/方法 | 流量特征 | 攻击场景 | 网站数量 | 结果[*] | ||
长度 | 时间 | 方向 | ||||||
Cheng [ | SSL | match | √ | √ | 封闭 | 489 | 96.7% | |
Sun [ | SSL | Jaccard | √ | √ | 开放 | 2 191 / 100 000 | 75% / 1.5% | |
Feghhi [ | TLS | DTW+kNN | √ | √ | 封闭 | 100 | 95% | |
Bissias [ | SSH | correlation | √ | √ | √ | 封闭 | 100 | 23% |
Liberatore [ | SSH | Jaccard, NB | √ | √ | 封闭 | 1 000 | 75% | |
Hermann [ | SSH | MNB | √ | √ | 封闭 | 775 | 96.7% | |
Cai [ | Tor | DLSVM | √ | √ | 封闭 | 100 | 87.3% | |
Wang [ | Tor | kNN | √ | √ | 封闭 | 100 | 91% | |
开放 | 100 / 5 000 | 85% / 0.6% | ||||||
Panchenko [ | Tor | SVM | √ | √ | 封闭 | 100 | 91.4% | |
开放 | 100 / 9 000 | 96.6 / 9.6% | ||||||
Hayes [ | Tor | RF+kNN | √ | √ | √ | 开放 | 30/100 000 | 85% / 0.02% |
Abe [ | Tor | SDAE | √ | 封闭 | 100 | 88% | ||
开放 | 100 / 7 000 | 86% / 2% | ||||||
Rimmer [ | Tor | SDAE, CNN, LSTM | √ | 封闭 | 900 | 94.3% | ||
开放 | 200 / 400 000 | 71.3% / 3.4% | ||||||
Sirinam [ | Tor | CNN | √ | 封闭 | 95 | 98.3% | ||
开放 | 95 / 20 000 | 95.7 / 0.7% | ||||||
Bhat [ | Tor | CNN | √ | √ | 封闭 | 100 | 97.8% | |
开放 | 100 / 10 000 | 89.2% / 1.1% | ||||||
注:“√”表示攻击利用了该流量特征。[*]对于开放世界场景,结果为准确率;对于封闭世界场景,结果为真阳率和假阳率。 |
表2
手机应用指纹攻击方法比较Table 2 Comparison of mobile app fingerprinting attack methods"
作者 | 攻击目标 | 模型/方法 | 流量特征 | 准确率 | ||
类型 | 数量 | 载体 | 数量 | |||
St?ber [ | 应用 | 14 | kNN, SVM | burst | 23 | 90% |
Wang [ | 应用 | 13 | RF | burst | 20 | 93% |
Alan [ | 应用 | 1 595 | MNB | packet | 64 | 88% |
Conti [ | 特定行为 | 50 | DTW+HC+RF | flow | 小于600 | 95% |
Saltaformaggio [ | 特定行为 | 35 | k-means+SVM | flow | 26 | 78% |
Taylor [ | 应用 | 110 | RF, SVM | flow | 40 | 99% |
Taylor [ | 应用 | 65 | RF | flow | 54 | 96% |
表3
针对加密流量的击键攻击方法比较Table 3 Comparison of keylogging attack methods against encrypted traffic"
作者 | 攻击目标 | 模型/方法 | 流量特征 | 信息增益 | 准确率 | |||
类型 | 数量 | 方向 | 长度 | 时间 | ||||
Song [ | 输入口令 | 368 | HMM+Viterbi | 双向 | √ | √ | 5.7 bit | |
Chen [ | 点击表项 | 2 670 | ASR | 双向 | √ | 10 bit | ||
Schaub [ | 查询关键字 | 10 / 6 812 | stochastic algo | 下行 | √ | 34% | ||
Oh [ | 查询关键字 | 100 / 40 000 | SVM, kNN, RF | 双向 | √ | 48.2% | ||
Monaco [ | 查询字符串 | 4 000 | DFA+IC+RNN | 上行 | √ | √ | 15.8% | |
注:“√”表示攻击利用了该流量特征。 |
表4
针对加密流量的语音攻击方法比较Table 4 Comparison of attack methods against encrypted voice traffic"
作者 | 攻击目标 | 编码器 | 模型/方法 | 数据集 | 准确率 |
Wright [ | 语言 | Speex | n-gram | OGI电话录音 | 66% |
Wright [ | 特定语句 | Speex | Profile HMM | TIMIT音素语音 | 51% |
White [ | 语句 | Speex | max-entropy+HMM | TIMIT音素语音 | 10% |
Dupasquier [ | 特定语句 | SILK | Kalman-filter+DTW | 合成语音 | 60% |
Khan [ | 用户身份 | Speex | ENDs | CSLU讲话者语音 | 73% |
Backes [ | 用户身份 | Speex | VAD+3-tuples | 公开政治家演讲 | 48% |
表5
加密流量侧信道攻击的防御方法比较Table 5 Comparison of defense methods against side channel attack on encrypted traffic"
作者 | 防御目标 | 防御方法 | 隐藏信息 | 开销[*] | 防御率[*] | ||||
指纹 | 击键 | 语音 | 长度 | 时间 | 数量 | ||||
Song [ | √ | send timer | √ | ~500% | 100% | ||||
Sun [ | √ | exp padding | √ | ~100% | 64% | ||||
Liberatore [ | √ | MTU padding | √ | 145.3% | 88.7% | ||||
Wright [ | √ | √ | morphing | √ | 35.6% / 15.4% | 38.9% / 23.9% | |||
Panchenko [ | √ | Decoy | √ | √ | 85% | 94.4% | |||
Luo [ | √ | √ | HTTPOS | √ | √ | ~50% | 98% / 100% | ||
Dyer [ | √ | BuFLO | √ | √ | √ | 129.2% | 85.5% | ||
Juarez [ | √ | WTF-PAD | √ | √ | √ | 80% | 78% | ||
Wang [ | √ | Walkie-Talkie | √ | √ | 31% | 70.5% | |||
Monaco [ | √ | rand padding | √ | 0.3% | 47% | ||||
注:[*]开销表示额外的带宽消耗;防御率表示防御后攻击识别准确率降低的比例,即1-AccDefended/Acc。 |
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