7,583 research outputs found
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the
research community still lacks effective tools to easily monitor and understand
them. In particular, there is a need for techniques that are able to not only
track how prominently certain malicious actions, such as the exploitation of
specific vulnerabilities, are exploited in the wild, but also (and more
importantly) how these malicious actions factor in as attack steps in more
complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses
temporal word embeddings to model how attack steps are exploited in the wild,
and track how they evolve. We test ATTACK2VEC on a dataset of billions of
security events collected from the customers of a commercial Intrusion
Prevention System over a period of two years, and show that our approach is
effective in monitoring the emergence of new attack strategies in the wild and
in flagging which attack steps are often used together by attackers (e.g.,
vulnerabilities that are frequently exploited together). ATTACK2VEC provides a
useful tool for researchers and practitioners to better understand cyberattacks
and their evolution, and use this knowledge to improve situational awareness
and develop proactive defenses
De Novo Augmentation approach in NLP
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : λ°μ΄ν°μ¬μ΄μΈμ€λνμ λ°μ΄ν°μ¬μ΄μΈμ€νκ³Ό, 2023. 2. μ΄μν.With a deluge of text-based data available, the ability to automatically extract important information from the text data is crucial, especially extracting events from factual text data like news articles. Finding causal relations in texts has been a challenge since it requires methods ranging from defining event ontologies to
developing proper algorithmic approaches. In this paper, I developed a framework which classifies whether a given sentence contains a causal event. As my approach, I exploited an external corpus that has causal
labels to overcome the small size of the original corpus (Causal News Corpus) provided by task organizers.
Further, I employed a data augmentation technique utilizing PartOf-Speech (POS) based on my observation that some parts of speech are more (or less) relevant to causality. My approach especially improved the recall of detecting causal events in
sentences.1. Introduction 1
1.1 Study Background 1
1.2 Purpose of Research 2
2. Task and Dataset 2
3. Methodology 3
3.1 Causal Graph of the task 4
3.2 Data Augmentation via POS tagging 4
3.3 Model, pages 5
3.4 Experiment Setup 7
4. Result and Discussion 8
4.1 Result 8
4.2 Discussion 11
5. Future work and Conclusion 12
5.1 Future work 12
5.2 Conclusion 13
Bibliography 14μ
Attack2vec: Leveraging temporal word embeddings to understand the evolution of cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset
of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better
understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses.Accepted manuscrip
TC-GAT: Graph Attention Network for Temporal Causality Discovery
The present study explores the intricacies of causal relationship extraction,
a vital component in the pursuit of causality knowledge. Causality is
frequently intertwined with temporal elements, as the progression from cause to
effect is not instantaneous but rather ensconced in a temporal dimension. Thus,
the extraction of temporal causality holds paramount significance in the field.
In light of this, we propose a method for extracting causality from the text
that integrates both temporal and causal relations, with a particular focus on
the time aspect. To this end, we first compile a dataset that encompasses
temporal relationships. Subsequently, we present a novel model, TC-GAT, which
employs a graph attention mechanism to assign weights to the temporal
relationships and leverages a causal knowledge graph to determine the adjacency
matrix. Additionally, we implement an equilibrium mechanism to regulate the
interplay between temporal and causal relations. Our experiments demonstrate
that our proposed method significantly surpasses baseline models in the task of
causality extraction.Comment: Accepted by IJCNN 202
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