Much of human knowledge in cybersecurity is encapsulated within the
ever-growing volume of scientific papers. As this textual data continues to
expand, the importance of document organization methods becomes increasingly
crucial for extracting actionable insights hidden within large text datasets.
Knowledge Graphs (KGs) serve as a means to store factual information in a
structured manner, providing explicit, interpretable knowledge that includes
domain-specific information from the cybersecurity scientific literature. One
of the challenges in constructing a KG from scientific literature is the
extraction of ontology from unstructured text. In this paper, we address this
topic and introduce a method for building a multi-modal KG by extracting
structured ontology from scientific papers. We demonstrate this concept in the
cybersecurity domain. One modality of the KG represents observable information
from the papers, such as the categories in which they were published or the
authors. The second modality uncovers latent (hidden) patterns of text
extracted through hierarchical and semantic non-negative matrix factorization
(NMF), such as named entities, topics or clusters, and keywords. We illustrate
this concept by consolidating more than two million scientific papers uploaded
to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by
building a cyber-domain-specific KG.

从科学论文中提取本体学的等结构来构建多模态知识图谱，以及在网络安全领域应用层次 NMF 方法构建网络安全领域特定的知识图谱。

分层非负矩阵分解生成网络安全知识图

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative  Matrix Factorization

Classifiers deployed in the real world operate in a dynamic environment,
where the data distribution can change over time. These changes, referred to as
concept drift, can cause the predictive performance of the classifier to drop
over time, thereby making it obsolete. To be of any real use, these classifiers
need to detect drifts and be able to adapt to them, over time. Detecting drifts
has traditionally been approached as a supervised task, with labeled data
constantly being used for validating the learned model. Although effective in
detecting drifts, these techniques are impractical, as labeling is a difficult,
costly and time consuming activity. On the other hand, unsupervised change
detection techniques are unreliable, as they produce a large number of false
alarms. The inefficacy of the unsupervised techniques stems from the exclusion
of the characteristics of the learned classifier, from the detection process.
In this paper, we propose the Margin Density Drift Detection (MD3) algorithm,
which tracks the number of samples in the uncertainty region of a classifier,
as a metric to detect drift. The MD3 algorithm is a distribution independent,
application independent, model independent, unsupervised and incremental
algorithm for reliably detecting drifts from data streams. Experimental
evaluation on 6 drift induced datasets and 4 additional datasets from the
cybersecurity domain demonstrates that the MD3 approach can reliably detect
drifts, with significantly fewer false alarms compared to unsupervised feature
based drift detectors. The reduced false alarms enables the signaling of drifts
only when they are most likely to affect classification performance. As such,
the MD3 approach leads to a detection scheme which is credible, label efficient
and general in its applicability.

该论文提出了一种基于特征分类器的不依赖于数据分布或特定应用的无监督增量漂移检测算法，以实现在数据流中可靠地检测和适应概念漂移，并展示了在 6 个漂移诱导数据集和 4 个来自网络安全领域的附加数据集上的实验评估结果，表明该算法与无监督特征漂移检测器相比具有更少的误警，可以信任且适用性广泛。