CS 259D Continuous Authentication for Mouse Dynamics: A Pattern-Growth Approach

作者:Leo

  • 背景知识
  • 论文目标和贡献
  • 数据(鼠标行为数据)
    • 数据来源
    • 鼠标行为分析
    • 鼠标行为模式挖掘
      • 标记定义
      • 数据挖掘方法
      • 行为模式参考的生成与匹配
      • 行为模式分析
  • 特征
    • 从挖掘到的模式中构造特征
    • 特征实例研究
  • 实施检测
  • 局限性
  • 参考资料

背景知识

  • 用户验证机制
    • 只在最初的登录会话时验证
    • 持续多次(重新)验证
      • 被动验证
      • 对用户透明
        • 不影响使用体验
  • 鼠标动力学
    • 内在行为变量
      • 内在的人为因素
        • 用户的生理或情感状态
      • 外部环境因素
        • 软件环境、任务、交互模式
    • 缺乏恶意用户的数据
    • 没用公开的鼠标动力学数据
  • 鼠标行为模式
    • 经常重复对的行为片段
    • 固定的连续操作序列
  • 猜想
    • 观察到
      • 从行为模式中提取的测量方法比用整体的行为中提取的要好
    • 假设
      • 固定重复的行为模式
        • 提供了更加稳定可以区分的特征和测量方法
        • 允许更精确地从鼠标行为的可辨别组分中提取特征
  • 单一分类
    • 只有合法用户模式可以获得
    • 同时使用合法用户和伪装用户训练分类器并不现实
      • 在实际操作中,伪装用户不可胜数

论文目标和贡献

  • 使用基于模式增长的数据挖掘方法来提取固定重复的鼠标行为模式
  • 单一分类器
  • 建立一个鼠标行为数据集
  • 开发一个简单高效的用户持续验证方法

数据(鼠标行为数据)

数据来源

  • 设定实验环境
  • 相同的电脑配置
  • 所有的用户都是大学生(有些人的专业是计算机科学)
  • 28 名参与者
    • 每用户大约90,000个鼠标动作
    • 30 次会话
      • 每次30分钟
      • 上网冲浪,文字处理,在线聊天,编程,在线游戏
      • 每个参与者记录30-60天
  • 数据记录
    • 事件类型(例如,鼠标移动/点击),位置,时间戳,应用程序信息

鼠标行为分析

  • 鼠标事件
    • 系统消息发送到接收应用程序
      • 通知当前光标位置和鼠标按钮状态
    • 类型
      • Mouse Down
      • Mouse Up
      • Mouse Wheel
      • Mouse Move
  • 鼠标动作
    • 单击
      • 按下鼠标,然后将松开鼠标
    • 双击
      • 按下、松开、按下、松开
    • 普通运动
      • 普通移动,没有点击
    • 指向并点击
      • 移动,然后单击/双击
    • 拖放动作
      • 按下,移动,松开
    • 沉默
      • 无操作
  • 鼠标动作记录为鼠标操作
  • 鼠标操作
    • 记录元组:
      • <action-type, application-type, screen-area, window-position, timestamp>
  • 鼠标行为模式:固定重复的鼠标行为模式
    • 细微习惯行为模式
      • 刺激GUI交互的潜意识/习惯性因素
    • 有计划的任务中的行为模式
      • 特定应用程序的操作习惯(例如,使用应用程序的特定功能)
        • 例如:在文字处理应用程序中创建新文档

鼠标行为模式挖掘

标记定义

  • \(I = {i_1 , i_2, …, i_n}\), 所有鼠标操作的集合
  • \(\mathrm{operation}\text{-}\mathrm{set}\): 一个鼠标操作集
    • 例如:\({(1,3,4,0), (2,1,4,0), (3,1,4,0)}\)
  • \(s\),按照用户 ID 和时间戳排序的序列
    • \(s = {s_1, s_2, …, s_k}\),其中每个 \(s_j\)
      • 一个 \(\text{operation-set}\) (subset of \(I\)), \(s_j \subset I\),其中 \(1 \leq j \leq l\)
      • 被称为序列 \(s\) 的一个元素
      • \(s_j = \langle x_1, x_2, …, x_m \rangle\),其中每个 \(x_k\) 是一个鼠标怕操作
    • 例如:
      • \(s = {\langle(1,3,4,0)\rangle, \langle(1,3,4,0), (2,1,4,0), (3,1,4,0)\rangle, \langle(2,1,2,1)\rangle}\)
      • \(\mathrm{length}(s) = 5\)
  • \(\alpha\),\(s\) 的一个子序列
    • 例如:
      • \({\langle(1,3,4,0), (2,1,4,0), (3,1,4,0)\rangle}\) 的子序列是\({\langle(1,3,4,0)\rangle}\)
  • \(L\),序列长度:鼠标操作实例的个数
    • \(\text{L-sequence}\):长度为 \(L\) 的序列
  • \(S\),鼠标操作序列数据库
    • 元组 \(\langle ID, sid, s \rangle\) 的集合
      • \(ID\),用户 ID
      • \(sid\),\(ID\) 的序列
      • \(s\), 序列
  • \(\mathrm{Support_S} (\alpha)\) = 数据集 \(S\) 中包含 \(\alpha\) 的元组的数量
  • \(\mathrm{Support_S} \geq \xi\) 可被称为序列模型
    • \(\xi\) 一个给定的阈值
  • 问题陈述
    • 鼠标行为模式挖掘是
      • 输入
        • 鼠标操作序列 \(S\)
        • 设定最小阈值 \(\xi\)
      • 输出
        • \(S\) 中所有固定重复的鼠标行为模式的完整集合

数据挖掘方法

  • PrefixSpan
    • Mining Sequential Patterns by Pattern­ Growth: The PrefixSpan Approach (2004)
    • 将数据库映射到一组较小的数据库中
      • 基于已经取得的模式
    • 在每个已映射的数据空中挖掘本地重复模式

行为模式参考的生成与匹配

  • 创建”基线”,由正常行为模式生成
    • 对于每个用户
      • 从每次会话中挖掘行为模式
      • 收集所有模式作为行为模式参考
  • 模式匹配
    • 一个给定的新操作序列与已挖掘出的序列相配
      • 搜索已有模式
      • 输出所有匹配模式

行为模式分析

  • 设置最小 support 值为 \(8\%\)
    • 以此权衡最小 support 和真是有效的行为模式

特征

  • 固定重复的行为特征无法被直接使用

从挖掘到的模式中构造特征

  • 点击时间
    • 用户执行点击操作花费的时间
    • 单击:总时间的平均值,标准差
    • 双击:总时间,以及三个间隔的平均值,标准差
  • 运动速度
    • 不同类型的鼠标移动的平均速度
    • 24种类型:8个方向,3个距离范围
  • 运动加速度
    • 不同类型鼠标移动的平均加速度
    • 与运动速度类似
  • 最大速度在速度曲线上的相对位置
    • 例如:0.5 意味着最大速度出现在运动轨迹的中间
  • 总共92个特征
    • 20个点击相关的特征
    • 24个运动相关的特征
      • 只包含普通运动和指向并点击
    • 24个加速度相关的特征
    • 24个最大速度相关的特征

特征实例研究

  • 行为模式特征的稳定性
    • 特征的偏斜可能来自
      • 环境因素
      • 人为因素
    • 例子
      • 查找文档(不确定文档在哪)
    • 核密度估计
      • 一种无参数估计随机变量概率密度函数的方法
    • 计算每个特征的概率密度函数,特征来自
      • 行为模式
      • 整体行为
  • 从行为模式中判别特征
    • 从行为模式中提取出的特征确实优于整体行为中提取的特征
      • 更稳定
      • 更可辨
  • 特征跨项目的统计分布

实施检测

  • 邻近算法
    • \(k = 3\)
    • 异常分数
      • 训练和测试特征向量之间的 Mahalanobis 距离
  • 神经网络
    • 单一隐藏层
      • 输入神经元,\(p\)
      • 隐藏神经元,\(\lfloor2p/3\rfloor\)
      • 输出神经元,\(1\)
    • 用每一个输入特征向量训练检测器使得输出为1.0
    • 测试向量输入神经网络,输出值大约为 1.0 或 -1.0
  • 单分类 SVM
    • RBF kernel

局限性

  • 样品仅采样自特定的机构(大学)
  • 没有攻击者的样本
  • 每次部署都需要收集数据和训练模型

参考资料

  • Continuous Authentication for Mouse Dynamics: A Pattern-Growth Approach (C. Shen et al., 2012)
  • CS 259D Lecture 5

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