摘要:基于交互行为的用户特征提取和身份认证方法是一种重要的身份识别方式，但高频用户的交互行为模式和操作习惯相对稳定，易被欺诈者模仿，使得现有模型对此类欺诈行为的误判较高.如何使得用户行为主动平滑变化且可区分，成为解决上述问题的关键.针对此问题，提出一种基于个体交互行为系统平滑干预模型：首先，根据用户历史交互行为日志从多个维度得到用户的交互行为变化趋势；然后，结合行为的稳定性和偏向性提出行为时域漂移算法（TDDA），为每个用户确定行为引导的时机；最后，基于Petri网提出交互行为重构系统干预模型，在系统中的非关键路径叠加行为触发因素，引导用户产生新的交互行为习惯.实验证明了提出的方法能够很好地引导用户行为平滑变化，且产生足够的区分性，使得行为伪装异常检测场景下模型的准确性显著提高.



Abstract:User feature extraction and identity authentication methods based on interactive behavior are an important method of identity recognition. However, for high-frequency users, the interactive behavior patterns and operating habits are relatively stable, which are easily imitated by fraudsters and making the existing models have a higher misjudgment. The key point to solve the above problems is to make the user's behavior change smoothly and distinguishably. This study proposes a smooth intervention model based on an individual interactive behavior system to handle it. Firstly, according to the user history web behavior log, the change trend of user interaction behavior is obtained from multiple dimensions. Then, combined with the stability and deviation of the behavior, the time-domain drift algorithm (TDDA) is proposed to determine the behavior guidance time of each user. Finally, an intervention model for interactive behavior reconstruction systems is proposed, which superimposes behavior trigger factors on non-critical paths in the system to guide users generating new interactive behavior habits. Experiments prove that the method proposed in this study could guide the user's behavior to change smoothly and produce sufficient distinction to significantly advance the model accuracy in the scenario of behavior camouflage anomaly detection.



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