基于数据挖掘和优化算法的企业员工异常行为的检测和预测
1School of Management, Changchun University of Finance and Economies, Changchun, 130000, Jilin, China. pink209@163.com.
Scientific reports
|July 31, 2024
概括
这项研究引入了用于检测内部威胁的新型模型. 精灵搜索算法优化的BP神经网络 (SBP) 识别异常行为,而LSTM网络 (SLSTM) 预测辞职,增强组织安全.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 组织行为 组织行为
背景情况:
- 内部员工的行为给组织带来了重大的安全挑战.
- 积极的威胁监测和预警系统对于减轻损害至关重要.
- 分析行为数据可以为组织安全管理提供有效的解决方案.
研究的目的:
- 开发和整合用于检测异常员工行为和预测辞职的模型.
- 通过数据挖掘和机器学习增强组织威胁检测策略.
- 提高识别有风险的员工的效率和准确性.
主要方法:
- 数据挖掘被用来构建内部威胁的知识图.
- 针对异常行为检测,开发了一个通过SSA (Sparrow Search Algorithm) 优化的BP神经网络 (SBP).
- 使用SBP优化了一个LSTM网络,用于预测员工辞职意图 (SLSTM).
主要成果:
- SBP模型有效地检测到异常的员工行为.
- 该SLSTM模型准确地预测了员工辞职意图.
- 与其他算法相比,集成的SBP和SLSTM模型表现出优异的性能,误判率降低.
结论:
- 开发的SBP和SLSTM模型为组织提供了全面的威胁检测技术.
- 这种方法显著提高了检测效率和更新速度的异常员工行为.
- 调查结果表明,内部威胁对组织安全的影响得到了实质性的缓解.
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