Context2Vector: Accelerating security event triage via context representation learning

Liu, Jia; Zhang, Runzi; Liu, Wenmao; Zhang, Yinghua; Gu, Dawei; Tong, Mingkai; Wang, Xingkai; Xue, Jianxin; Wang, Huanran

doi:10.1016/j.infsof.2022.106856

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The integration of contextual information stands as one of the methods for optimizing event feature representations within life stories [14]. Among the information types, temporal contextual information holds pivotal significance [15].…”

Section: Event Feature Refinement With Graph-enhanced Methodologiesmentioning

confidence: 99%

Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

Gui,

Yang,

Tang

et al. 2024

Applied Sciences

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The life stories of older adults encapsulate an array of personal experiences that reflect their care needs. However, due to inherent fuzzy features, fragmented natures, repetition, and redundancies, the practical application of the life story approach poses challenges for caregivers in acquiring and comprehending these narratives. Addressing this challenge, our study introduces a novel approach called Life Story Hierarchies with Graph-Enhanced Event Feature Refinement (LSH-GEFR). LSH-GEFR constructs a bilayer graph. Firstly, the event element map leverages intricate relationships between event elements to extract environmental features, providing a detailed context for understanding each event element. Secondly, the event map explores the complex web of relationships between the events themselves, allowing LSH-GEFR to generate a comprehensive understanding of each event and enhance its representation. Subsequently, we conducted experiments on different datasets and found that, in comparison with four advanced event tree generation methods, the proposed LSH-GEFR method outperformed them in terms of path coherence, branch reasonableness, and overall readability when generating life story hierarchies. Over 84.91% of the structured life narratives achieved readability, marking a 5.96% increase over the best-performing approach at the baseline.

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Section: Event Feature Refinement With Graph-enhanced Methodologiesmentioning

confidence: 99%

Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

Gui,

Yang,

Tang

et al. 2024

Applied Sciences

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“…To solve this problem, network representation learning [30][31] is formally applied in the field of the software defect prediction. The software network graph is first constructed based on the dependencies existing between the software modules, and through representation learning, the graph information is effectively characterized and more important structural features are extracted.…”

Section: Introductionmentioning

confidence: 99%

Software defect prediction based on counterfactual explanations

Zheng

Chen

et al. 2022

Preprint

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Software defect prediction is critical to ensure the software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. The existing defect prediction models are mainly divided into two categories: one is the models constructed based on the artificial statistical features and the other is the models constructed based on the semantic features. DP-CNN [29] is one of the best defect prediction models, because it combines both the artificial statistical features and semantic features, so its performance is greatly improved compared with traditional defect prediction models. In this paper, we are inspired by the DP-CNN model and make the following two improvements: firstly, we use a new network representation technique Struc2vec to mine the information existing between software modules, which specializes in learning node representations from structural identity and can further extract the structural features associated with defects. We let the DP-CNN model again incorporate the newly mined structural features. Then, we propose a feature selection method based on counterfactual explanations which preserves reasonable causal relationships between features and thus produces a better subset of features compared to traditional feature selection methods. We used it to optimize the artificial statistical features inside the DP-CNN model. Based on the above methods, we propose a new hybrid defect prediction model DPS-CNN-STR. We evaluate our model on six open-source projects in terms of F1-score in defect prediction. The experimental results show that DPS-CNN-STR improves the state-of-the-art method by 3.6% on average.

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Combating alert fatigue with AlertPro: Context-aware alert prioritization using reinforcement learning for multi-step attack detection

Wang,

Yang,

Liang

et al. 2024

Computers & Security

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Context2Vector: Accelerating security event triage via context representation learning

Cited by 6 publications

References 4 publications

Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

Software defect prediction based on counterfactual explanations

Combating alert fatigue with AlertPro: Context-aware alert prioritization using reinforcement learning for multi-step attack detection

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