Explainable Artificial Intelligence (XAI) has become a widely discussed
topic, the related technologies facilitate better understanding of conventional
black-box models like Random Forest, Neural Networks and etc. However,
domain-specific applications of XAI are still insufficient. To fill this gap,
this research analyzes various machine learning models to the tasks of binary
and multi-class classification for intrusion detection from network traffic on
the same dataset using occlusion sensitivity. The models evaluated include
Linear Regression, Logistic Regression, Linear Support Vector Machine (SVM),
K-Nearest Neighbors (KNN), Random Forest, Decision Trees, and Multi-Layer
Perceptrons (MLP). We trained all models to the accuracy of 90\% on the
UNSW-NB15 Dataset. We found that most classifiers leverage only less than three
critical features to achieve such accuracies, indicating that effective feature
engineering could actually be far more important for intrusion detection than
applying complicated models. We also discover that Random Forest provides the
best performance in terms of accuracy, time efficiency and robustness. Data and
code available at this https URL

本研究通过使用遮挡敏感性方法，对二元和多类别分类的入侵检测任务中的各种机器学习模型进行了分析，使用相同的数据集。我们发现，大多数分类器仅利用少于三个关键特征就能实现高准确性，表明有效的特征工程对于入侵检测可能比应用复杂模型更为重要。我们还发现，在准确性、时间效率和鲁棒性方面，随机森林提供了最佳性能。

可解释人工智能用于入侵检测模型的比较分析

Explainable AI for Comparative Analysis of Intrusion Detection Models

Jointly harnessing complementary features of multi-modal input data in a
common latent space has been found to be beneficial long ago. However, the
influence of each modality on the models decision remains a puzzle. This study
proposes a deep learning framework for the modality-level interpretation of
multimodal earth observation data in an end-to-end fashion. While leveraging an
explainable machine learning method, namely Occlusion Sensitivity, the proposed
framework investigates the influence of modalities under an early-fusion
scenario in which the modalities are fused before the learning process. We show
that the task of wilderness mapping largely benefits from auxiliary data such
as land cover and night time light data.

本研究提出了一种深度学习框架，用于对多模式地球观测数据进行模态级别的解释。该框架利用可解释的机器学习方法，即遮挡敏感性，研究了模态在学习过程之前融合的情况下对模型决策的影响，并证明了荒野映射任务受到土地覆盖和夜间光数据等辅助数据的极大益处。