Spearman Correlation Heatmaps After Feature Selection

Description:
This is a Spearman Correlation Heatmap of the 32 features used for machine learning and deep learning models in cybersecurity. The diagonal cells are perfect self-correlation (value = 1) and the off-diagonal cells are pairwise correlations between features. Since there are no strong correlations (close to 1 or -1) we removed the redundant or irrelevant features, so each selected feature brings unique and independent information to the model. Feature selection is key in building cyber intrusion detection systems as it reduces computational overhead, simplifies the model and improves accuracy and robustness. This is part of the systematic feature engineering process to optimize datasets for anomaly detection, network traffic analysis and intrusion detection. Researchers in AI for cybersecurity can use this to build more interpretable and efficient models to detect in large scale networks. This figure shows the importance of correlation analysis for high dimensional datasets and contributes to cyber, data science and machine learning.

Why It Matters:
Reduces overfitting in machine learning models.
Improves computational efficiency for large-scale datasets.
Enhances feature interpretability for robust cybersecurity solutions.

Keywords:
Spearman Correlation Heatmap, Feature Selection, Intrusion Detection System, Cybersecurity, Machine Learning, Deep Learning, Anomaly Detection, Network Traffic Analysis, Artificial Intelligence in Cybersecurity, Dataset Optimization, Feature Engineering for Cyber Threats

References:
This file pertains to our research study, which has been accepted for publication in the Scientific and Technical Journal of Information Technologies, Mechanics and Optics. The study is titled:
"Enhancing and Extending CatBoost for Accurate Detection and Classification of DoS and DDoS Attack Subtypes in Network Traffic."

https://doi.org/10.1109/ICSIP61881.2024.10671552
https://doi.org/10.24143/2072-9502-2024-3-65-74

描述：
本数据集展示了用于网络安全中机器学习和深度学习模型的32个特征的Spearman相关系数热图。对角线单元格代表完美的自相关（值为1），而非对角线单元格则表示特征间的成对相关。鉴于不存在强烈的关联（接近1或-1），因此移除了冗余或不相关的特征，以确保每个选定的特征都能为模型提供独特且独立的信息。特征选择对于构建网络入侵检测系统至关重要，因为它可以降低计算开销，简化模型，并提高准确性和鲁棒性。这是系统化特征工程流程的一部分，旨在优化数据集以实现异常检测、网络流量分析和入侵检测。网络安全领域的AI研究人员可以利用此数据集构建更具可解释性和效率的模型，以检测大规模网络中的入侵。
本图展示了关联分析在高维数据集中的重要性，并为网络安全、数据科学和机器学习领域做出了贡献。

重要性：
减少机器学习模型中的过拟合。
提高大规模数据集的计算效率。
增强特征的可解释性，以提供鲁棒的网络安全解决方案。

关键词：
Spearman相关系数热图，特征选择，入侵检测系统，网络安全，机器学习，深度学习，异常检测，网络流量分析，网络安全中的AI，数据集优化，网络安全威胁特征工程。

参考文献：
本文件属于我们已接受在《信息科技、机械与光学科学技术期刊》上发表的研究成果。该研究题为：

'增强并扩展CatBoost，以准确检测和分类网络流量中的DoS和DDoS攻击子类型。'

[https://doi.org/10.1109/ICSIP61881.2024.10671552](https://doi.org/10.1109/ICSIP61881.2024.10671552)
[https://doi.org/10.24143/2072-9502-2024-3-65-74](https://doi.org/10.24143/2072-9502-2024-3-65-74)