security-decision-drift - Reproducible research artifact for studying defens

项目说明

Security Decision Drift

Reproducible research artifact for studying defensi security decision drift.

O

This repository accompanies the ICCSAI research work on Security Decision Drift.

The central question is whether a defensi can alter subsequent security e and influence a later security decision, even when the external state is otherwise held constant.

Research Artifact

The repository will contain:

E Boundary

The controlled simulation is the primary experimental e.

Public cyber-trace e is treated separately from deployment-le because public datasets do not provide complete recorded defender inter histories.

Author

Prince

UNSW-NB15 E

The proposed decision mechanism was additionally e using the
publicly a UNSW-NB15 network intrusion dataset.

The dataset was used as real network-traffic data, while inter
effects were introduced through a controlled experimental layer because
UNSW-NB15 does not contain historical defensi records.

Dataset

Baseline

Reality Check

False actions decreased from 2,189 to 1,123,
representing approximately a 48.7% reduction.

Decision drift rate was 4.91%.

The inter adjustment is a controlled experimental parameter
and is not claimed to be directly observed in UNSW-NB15.

Reproduction

Install dependencies:

```bash
python -m pip install pandas numpy scikit-learn pytest

项目信息

开发语言：Python

Star 数：1

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Reproducible research artifact for studying defensive-intervention-induced security decision drift.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。