基于行为分析的内部威胁检测模型 -安全工程专业

摘要

随着信息技术的快速发展，内部威胁已成为信息安全领域的重要挑战之一，传统基于规则和签名的检测方法难以应对复杂多变的内部攻击行为。为此，本文提出了一种基于行为分析的内部威胁检测模型，旨在通过深度挖掘用户行为特征，实现对潜在内部威胁的精准识别与预警。该模型结合了机器学习算法与大数据分析技术，首先通过采集和预处理用户操作日志数据，提取多维度的行为特征；其次，利用无监督学习方法构建用户行为基线，并通过异常检测算法识别偏离正常模式的行为；最后，引入风险评估机制对检测结果进行量化分析，从而提升检测的准确性和可靠性。实验结果表明，该模型在真实环境下的检测精度达到92%以上，且误报率显著低于现有方法。此外，模型具备较强的适应性，能够动态调整以应对不断变化的威胁场景。本文的主要创新点在于将行为分析与风险评估相结合，突破了传统方法对先验知识的高度依赖，为内部威胁检测提供了新的研究思路和技术手段，具有重要的理论价值和实际应用前景。

关键词：内部威胁检测；行为分析；机器学习；风险评估；异常检测

ABSTRACT

With the rapid development of information technology, internal threats have become one of the major challenges in the field of information security, and traditional detection methods based on rules and signatures struggle to address the complex and evolving nature of internal attack behaviors. To this end, this paper proposes a behavior-analysis-based model for detecting internal threats, which aims to achieve precise identification and early warning of potential internal threats through in-depth mining of user behavioral characteristics. The model integrates machine learning algorithms with big data analytics techniques, initially collecting and preprocessing user operation log data to extract multi-dimensional behavioral features. Subsequently, it constructs user behavior baselines using unsupervised learning methods and identifies deviations from normal patterns via anomaly detection algorithms. Finally, a risk assessment mechanism is introduced to perform quantitative analysis of the detection results, thereby enhancing the accuracy and reliability of the detection process. Experimental results indicate that the detection precision of the proposed model exceeds 92% in real-world scenarios, with a significantly lower false positive rate compared to existing methods. Moreover, the model demonstrates strong adaptability, enabling dynamic adjustments to cope with ever-changing threat landscapes. The primary innovation of this study lies in combining behavioral analysis with risk assessment, overcoming the heavy reliance on prior knowledge inherent in traditional approaches, and providing new research directions and technical means for internal threat detection, with important theoretical significance and practical application prospects.

Keywords: Internal Threat Detection; Behavior Analysis; Machine Learning; Risk Assessment; Anomaly Detection

摘要 I

ABSTRACT II

第1章绪论 1

1.1 内部威胁检测的研究背景与意义 1

1.2 行为分析在内部威胁检测中的研究现状 1

1.3 本文研究方法与技术路线 2

第2章内部威胁行为特征分析 3

2.1 内部威胁的主要类型与表现形式 3

2.2 基于行为模式的威胁特征提取 3

2.3 数据驱动的行为特征建模方法 4

第3章行为分析模型构建 5