用变分自编码器提取的隐空间特征，提升恶意软件检测效率与准确率。
Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers
- 通过变分自编码器学习恶意软件的隐空间表示，作为机器学习分类器输入。
- 集成方法LightGBM和随机森林在多个测试中表现最优，准确率显著提升。
- 该方法降低计算成本，减少调参需求，适合实际部署的网络安全场景。
本文评估了五种机器学习分类器——决策树、朴素贝叶斯、LightGBM、逻辑回归和随机森林——在使用变分自编码器从恶意软件数据集中学习到的隐空间表示上的性能。在不同训练-测试划分及不同随机种子下的实验结果表明，所有模型均能有效检测恶意软件，其中集成方法（LightGBM和随机森林）表现略优。此外，使用隐特征可降低模型计算开销，并减少对复杂超参数调优的需求，提升模型部署效率。统计检验证实这些改进具有显著性，因此，将隐空间表示与传统分类器结合用于有效恶意软件检测在实践中具有重要意义。
原文摘要 · Abstract (English)
This paper assesses the performance of five machine learning classifiers: Decision Tree, Naive Bayes, LightGBM, Logistic Regression, and Random Forest using latent representations learned by a Variational Autoencoder from malware datasets. Results from the experiments conducted on different training-test splits with different random seeds reveal that all the models perform well in detecting malware with ensemble methods (LightGBM and Random Forest) performing slightly better than the rest. In addition, the use of latent features reduces the computational cost of the model and the need for extensive hyperparameter tuning for improved efficiency of the model for deployment. Statistical tests show that these improvements are significant, and thus, the practical relevance of integrating latent space representation with traditional classifiers for effective malware detection in cybersecurity is established.
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