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对基于CNN的内部威胁检测数据不平衡解决技术的比较评估
Taher Al-Shehari1, Mohammed Kadrie2, Mohammed Nasser Al-Mhiqani3
1Computer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia. talshehari.c@ksu.edu.sa.
Scientific reports
|October 21, 2024
概括
适应合成采样 (ADASYN) 与卷积神经网络 (CNN) 结合,显著提高了在不平衡的网络安全数据集上内部威胁检测的准确性,优于其他过度采样技术.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 内部威胁是一个主要的网络安全挑战,需要强大的检测.
- 不平衡的数据集在网络安全中很常见,阻碍了模型的性能.
- 现有的研究缺乏关于内部威胁检测最佳数据失衡技术的共识.
研究的目的:
- 为了比较评估基于卷积神经网络 (CNN) 的内部威胁检测的数据失衡技术.
- 在不平衡的数据集上提高内部威胁检测模型的准确性和稳定性.
- 解决关于特定数据失衡技术优越性的缺乏共识的问题.
主要方法:
- 整合CNN与三种数据失衡技术:合成少数人过量采样技术 (SMOTE),边界-SMOTE和自适应合成采样 (ADASYN).
- 使用基于人类行为的数据集 (CERT),包括IT活动进行分析.
- 混合模型的比较评估 (CNN + 不平衡解决技术) 与最先进的研究相比.
主要成果:
- 适应合成采样 (ADASYN) 与CNN相结合,实现了96%的ROC曲线.
- 在提高检测准确度方面,ADASYN在SMOTE和Borderline-SMOTE上表现优越.
- 混合型号的比较基于ROC,回忆和准确度指标.
结论:
- 在与CNN结合使用时,ADASYN是最有效的数据失衡技术,用于内部威胁检测.
- 这些发现有助于推进探测网络安全内部威胁的方法.
- 与ADASYN集成的基于CNN的模型为不平衡的数据集提供了更好的准确性和稳定性.
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