Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition

• URL: http://arxiv.org/abs/2401.10337v3
• Date: Tue, 30 Jan 2024 11:40:07 GMT
• Title: Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition
• Authors: Tu Nguyen, Nedim \v{S}rndi\'c, Alexander Neth
• Abstract summary: Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two. We propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism.
• Score: 49.536368818512116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain, described encyclopedically in textual knowledge bases. Identifying TTPs in cybersecurity writing, often called TTP mapping, is an important and challenging task. Conventional learning approaches often target the problem in the classical multi-class or multilabel classification setting. This setting hinders the learning ability of the model due to a large number of classes (i.e., TTPs), the inevitable skewness of the label distribution and the complex hierarchical structure of the label space. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two, thus reducing the complexity of competing solely over the large labeling space. To that end, we propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism, facilitating the learning process of the matching model despite constrained resources.

Related papers

• Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition [56.968108142307976]
  Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. Most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects.
  arXiv  Detail & Related papers  (2024-02-24T13:00:54Z)
• Robust Representation Learning for Unreliable Partial Label Learning [86.909511808373]
  Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels. We propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively.
  arXiv  Detail & Related papers  (2023-08-31T13:37:28Z)
• Learning Semantic Correspondence with Sparse Annotations [66.37298464505261]
  Finding dense semantic correspondence is a fundamental problem in computer vision. We propose a teacher-student learning paradigm for generating dense pseudo-labels. We also develop two novel strategies for denoising pseudo-labels.
  arXiv  Detail & Related papers  (2022-08-15T02:24:18Z)
• A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation [42.0958430465578]
  We study the partial multi-label (PML) image classification problem. Existing PML methods typically design a disambiguation strategy to filter out noisy labels. We propose a deep model for PML to enhance the representation and discrimination ability.
  arXiv  Detail & Related papers  (2022-07-06T02:49:02Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels [26.542718087103665]
  SemiMatch is a semi-supervised solution for establishing dense correspondences across semantically similar images. Our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target. In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks, especially on PF-Willow by a large margin.
  arXiv  Detail & Related papers  (2022-03-30T03:52:50Z)
• Universalizing Weak Supervision [18.832796698152492]
  We propose a universal technique that enables weak supervision over any label type. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.
  arXiv  Detail & Related papers  (2021-12-07T17:59:10Z)
• Reinforcement Learning-powered Semantic Communication via Semantic Similarity [13.569045590522316]
  We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. We show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. We put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement.
  arXiv  Detail & Related papers  (2021-08-27T05:21:05Z)
• Text Recognition in Real Scenarios with a Few Labeled Samples [55.07859517380136]
  Scene text recognition (STR) is still a hot research topic in computer vision field. This paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation. Our approach can maximize the character-level confusion between the source domain and the target domain.
  arXiv  Detail & Related papers  (2020-06-22T13:03:01Z)
• Network Cooperation with Progressive Disambiguation for Partial Label Learning [37.05637357091572]
  Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase. Existing methods ignore the disambiguation difficulty of instances and adopt the single-trend training mechanism. This paper proposes a novel approach "Network Cooperation with Progressive Disambiguation" (NCPD) By employing artificial neural networks as the backbone, we utilize a network cooperation mechanism which trains two networks collaboratively by letting them interact with each other.
  arXiv  Detail & Related papers  (2020-02-22T09:50:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.