CDBC:一种新的数据增强方法,基于改进的课间学习,用于暗网检测
Binjie Song1, Yufei Chang2, Minxi Liao3
1Academy of A&AD, Zhengzhou 450000, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
这项研究引入了基于Chebyshev远程的课堂间学习 (CDBC),以改善暗网流量检测. CDBC提高了准确性和回忆力,解决了网络安全中数据集不平衡所带来的挑战.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 互联网的增长和隐私问题导致了隐私保护技术的广泛使用.
- 这些技术无意中促进了暗网,这是一个由犯罪分子用于经济和情报目的的平台.
- 检测暗网流量至关重要,但由于数据严重不平衡,阻碍了准确性,因此具有挑战性.
研究的目的:
- 为了解决由于数据集不平衡而导致的暗网流量检测困难.
- 提出一种新的学习方法,切比什夫基于距离的课堂间学习 (CDBC),以提高检测准确性.
- 引入一个改进的暗网流量检测方法,利用CDBC.
主要方法:
- 拟议的基于Chebyshev距离的类间学习 (CDBC) 来分析暗网数据集的空间分布.
- 使用CDBC生成"差距数据"以优化数据集分布边界.
- 开发并测试了一种新的暗网流量检测方法,其中包括CDBC.
主要成果:
- CDBC显著提高了10多种现有方法的准确性,在基准数据集上达到99.99% (ISCXTor 2016,CIC-Darknet 2020).
- 与其他采样技术相比,拟议的方法显示出更高的性能.
- CDBC增强分类器召回,表明更有效地识别暗网流量.
结论:
- CDBC是优化数据集分布和改进暗网流量检测的有效方法.
- 由CDBC增强的拟议检测方法在网络安全方面为识别非法在线活动提供了显著的进步.
- 在不平衡的交通检测场景中,CDBC提供了一种有价值的工具来提高各种机器学习模型的性能.
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