Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (3): 170-187.doi: 10.19665/j.issn1001-2400.2021.03.022
• Cyberspace Security • Previous Articles Next Articles
ZENG Yong1(),WU Zhengyuan1(),DONG Lihua2(),LIU Zhihong1(),MA Jianfeng1(),LI Zan2()
Received:
2020-12-18
Online:
2021-06-20
Published:
2021-07-05
CLC Number:
ZENG Yong,WU Zhengyuan,DONG Lihua,LIU Zhihong,MA Jianfeng,LI Zan. Research on malicious traffic identification technology in encrypted traffic[J].Journal of Xidian University, 2021, 48(3): 170-187.
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文献 | 识别特征 | 识别模型 | 数据集 | 识别目的 | 详细描述 | 评价指标 |
---|---|---|---|---|---|---|
[ | 头部特征/ 负载特征 | CNN/SAE | ISCXVPN/Non-VPN | 二分类:vpn/非vpn 六分类:流量类型分类 | 对包的应用层有效载荷中可用信息的分析 | 召回率98% 召回率94% |
[ | 时序特征 | CNN | ISCXVPN/Non-VPN | 六分类:流量类型分类 | 使用CNN对时间相关特征的数据集进行分类 | 最高准确率94.6% |
[ | 头部特征 | CNN | ISCXVPN/Non-VPN | 二分类:vpn/非vpn 六分类:流量类型分类 | 流量头部特征可视化为图像 | 准确率99.9% 准确率94.9% |
[ | 头部特征 | CAE/ CNN | ISCXVPN/Non-VPN | 二分类:vpn/非vpn 六分类:流量类型分类 | 流量头部特征可视化为图像 | 准确率98.77% 准确率92.92% |
[ | 负载特征 | 胶囊神经 网络 | ISCXVPN/Non-VPN | 二分类:vpn/非vpn | 针对VPN加密报文序列进行高低熵划分 | 最高准确率99.87% |
[ | 负载特征/ 统计特征 | 人工神经 网络 | ISCXVPN/Non-VPN | 二分类:vpn/非vpn | 使用熵估计和人工神经网络相结合 | 最高准确率92.9% |
[ | 负载特征 | 神经网络 | IMG/ COCO[ | 二分类:加密/压缩流量分类 | 可以应用于单个数据包,而无需访问整个流 | 最高准确率94.72% |
[ | 头部特征/ 负载特征 | 深度学习 技术 | FB/私有 数据集 | 多分类: 加密手机流量分类 | 基于自动提取的特征建立分类器 | 优于已有方法 |
[ | 统计特征 | C4.5 | 私有数据集 | 多分类: 加密网络流量分类 | 同时使用统计特征和机器学习的方式 | 优于单一方法 |
[ | 统计特征 | 机器学习 | 私有数据集 | 四分类: 应用程序分类 | 消除了非高斯分布的特征,实现高精度 | 最高准确率97.4% |
[ | 头部特征/ 负载特征 | 有监督机 器学习 | 私有数据集 | 五分类: 应用程序分类 | 离线流量通过指纹识别应用程序 | 最高准确率99.64% |
[ | 统计特征 | 联合建模 | 私有数据集 | 多分类: 应用程序分类 | 联合建模用户行为模式等特征进行分类 | 最高准确率97% |
[ | 头部特征/ 负载特征 | 属性图 分类 | Campus/appScanner[ | 多分类: 应用程序分类 | 基于二阶马尔可夫链的属性感知加密流量分类 | 最高准确率93.49% |
[ | 头部特征/ 统计特征 | 随机森林 RF | 私有数据集 | 识别个人敏感信息 | 自动从IoT网络流量中推断出个人敏感信息 | 最高准确率99.8% |
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文献 | 识别算法 | 识别目的 | 详细描述 | 识别结果 |
---|---|---|---|---|
[ | SVM | 5种密码体制 | 在ECB、CBC模式下,使用SVM识别相同密钥和不同密钥加密生成的密文 | ECB模式优于CBC模式 |
[ | SVM | 5种密码体制 | DES、AES、TDES、RC5、Blowfish,共五种密码算法加密后的密文的直方图信息 | 平均识别准确率约25% |
[ | Adaboost | 5类分组密码 | 对生成的密文进行学习,将识别错误的样本数据再次训练 | 平均识别准确率约55% |
[ | 8种不同分类器 | 分组密码 | 使用8种不同分类器模型对分组密码进行识别 | RF效果的识别最佳 |
[ | 神经网络 | AES5种候选算法 | 使用神经网络进行密码体制识别 | 神经网络配置得当时,可以正确分类 |
[ | MLP | RC4密钥流和 随机密钥流 | 利用多层感知器学习特征并区分RC4密钥流和随机密钥流 | 平均识别准确率约69% |
[ | K-means | 5种分组密码 | 分析5种分组密码构成的密码体制,使用K-Means算法识别加密后的密文 | 若参数合理,密文的识别率约90% |
[ | RF | 密码体制分层识别 | 提出密码体制分层识别方案,选择机器学习的RF算法进行识别 | 多分类任务下准确率约60%~70% |
[ | RF | 二分类任务识别 | 涉及的序列密码算法包括Grain-128、RC4、Salsa | 两两识别的平均识别准确率约64% |
"
文献 | 识别特征 | 识别方法 | 数据集 | 识别目的 | 详细描述 | 准确率 |
---|---|---|---|---|---|---|
[ | 时空特征 | CNN | DARPA1998[ ISCX2012[ | 二分类:识别良性/ 恶意流量 | 利用深层神经网络学习原始流量数据的时空特征 | 最高99.96% |
[ | 头部特征 | 随机森林 | CTU-13[ MCFP | 二分类:识别良性/ 恶意流量 | 只从流量开始的大约8个数据包中提取特征,可以提前检测到恶意应用流量 | 未提及 |
[ | 头部特征 | 聚类 | 私有数据集 和Drebin[ | 多分类:识别恶意 应用流量 | 利用网络流量信息中多维应用层数据的恶意应用分类和检测 | 最高90% |
[ | 头部特征/ 统计特征 | 无监督学习 算法 | CICIDS 2017[ | 多分类:识别恶意 应用流量 | 用高斯混合模型和排序点识别聚类结构,计算恶意应用之间的距离 | 平均91.74% |
[ | 头部特征/ 负载特征 | CNN | USTC-TFC 2016 | 多分类:识别恶意 应用流量 | 使用原始流量数据转化为图像,利用CNN进行图像分类 | 平均99.41% |
[ | 头部特征/ 负载特征 | CNN | CTU-13 | 二分类:识别良性/ 恶意流量 | 从连接元数据中提取出上下文特征,使用Perlin噪声将特征编码到图像中 | 最高97% |
[ | 头部特征/ 负载特征 | Svm | 私有数据集 | 二分类:识别良性/ 恶意流量 | 将移动流量视为文档,使用NLP执行恶意应用检测 | 最高99.15% |
[ | 头部特征/ 负载特征 | 多视图神经 网络 | 私有数据集 | 二分类:识别良性/ 恶意流量 | 设计了一种利用应用程序访问的url来识别恶意应用程序的方法。 | 最高98.75% |
[ | 统计特征/ 头部特征 | C 4.5 | 私有数据集 | 多分类:识别恶意 应用流量 | 网络流量分析与C 4.5相结合识别Android恶意应用 | 最高99.65% |
[ | 统计特征 | 自组织特征 映射 | VirusTotal API | 多分类:识别恶意 应用流量 | 使用难以混淆的机器数据对恶意应用进行分类 | 最高89% |
[ | 统计特征 | 模块相似性 | 私有数据集 | 二分类:识别良性/ 恶意流量 | 基于行为的检测体系结构,使用相似性度量来检测入侵 | 未提及 |
[ | 统计特征 | 随机森林 | 私有数据集 | 多分类:识别恶意 应用流量 | 在加密的Android应用程序流量中指纹识别和实时识别 | 最高99% |
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