摘要
随着网络安全威胁不断演变,传统身份认证方法面临着日益严峻的挑战。文章以民航空管信息系统为应用背景,提出一种多因子身份可信持续认证方法。该方法包含两个阶段,第一阶段为登录时多因子身份可信认证,第二阶段为登录后多因子行为特征持续身份可信认证。在第二阶段持续认证中采用统计特征法和机器学习模型,增强对用户行为模式的实时监测和分析能力,提高异常行为检测的准确性。文章通过实验验证了多因子行为特征持续身份可信认证在单点异常和上下文异常场景下的有效性,证明了其在身份认证领域的可靠性和实用性。实验结果表明,该方法在提高系统安全性和降低被破解风险方面具有一定的优势。
As cybersecurity threats continue to evolve,traditional identity authentication methods face increasingly severe challenges.This paper proposed an innovative strategy for multi-factor continuous trustworthy identity authentication to address the growing complexity of security threats.The strategy included multi-factor authentication during the first stage login and multi-factor behavior characteristics continuous authentication after the second stage login.In the second stage of continuous authentication,statistical feature method and machine learning model were used to enhance the real-time monitoring and analysised of user behavior patterns,and improved the accuracy of abnormal behavior detection.Finally,the paper validated the effectiveness of the proposed continuous multi-factor behavioral characteristic-based trustworthy identity authentication through experiments under single-point anomaly and contextual anomaly scenarios,demonstrating its reliability and practicality in the field of identity authentication.The experimental results indicate that this method offers certain advantages in enhancing system security and reducing the risk of compromise.
作者
陈宝刚
张毅
晏松
CHEN Baogang;ZHANG Yi;YAN Song(Department of Automation,Tsinghua University,Beijing 100084,China;School of Traffic Management,People’s Public Security University of China,Beijing 100038,China)
出处
《信息网络安全》
CSCD
北大核心
2024年第11期1632-1642,共11页
Netinfo Security
关键词
民航空管
多因子行为特征
持续身份认证
异常检测
机器学习
air traffic control
multi-factor behavioral characteristics
continuous identity authentication
anomaly detection
machine learning
作者简介
陈宝刚(1975-),男,山东,高级工程师,硕士,主要研究方向为民航网络和数据安全、机器学习、大数据应用、神经网络;张毅(1964-),男,四川,教授,博士,主要研究方向为智能交通系统工程、交通大数据分析、交通群体协同控制;通讯作者:晏松(1990-),男,云南,讲师,博士,主要研究方向为智能交通、交通仿真与控制、交通大数据分析,ys1133@126.com。
