Adversarial Attack on Network Embeddings via Supervised Network Poisoning

• URL: http://arxiv.org/abs/2102.07164v1
• Date: Sun, 14 Feb 2021 14:47:39 GMT
• Title: Adversarial Attack on Network Embeddings via Supervised Network Poisoning
• Abstract summary: We study network embedding algorithms from an adversarial point of view and observe the effect of poisoning the network on downstream tasks. We propose VIKING, a supervised network poisoning strategy that outperforms the state-of-the-art poisoning methods by upto 18% on the original network structure.
• Score: 20.583791500831147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning low-level node embeddings using techniques from network representation learning is useful for solving downstream tasks such as node classification and link prediction. An important consideration in such applications is the robustness of the embedding algorithms against adversarial attacks, which can be examined by performing perturbation on the original network. An efficient perturbation technique can degrade the performance of network embeddings on downstream tasks. In this paper, we study network embedding algorithms from an adversarial point of view and observe the effect of poisoning the network on downstream tasks. We propose VIKING, a supervised network poisoning strategy that outperforms the state-of-the-art poisoning methods by upto 18% on the original network structure. We also extend VIKING to a semi-supervised attack setting and show that it is comparable to its supervised counterpart.

Related papers

• Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks [3.444915014411325]
  Graph Neural Networks (GNNs) represent a promising solution for Machine Learning (ML) based Network Intrusion Detection Systems (NIDS)<n>GNNs remain vulnerable to structural adversarial attacks, where an attacker perturbs the underlying network graph topology by injecting edges or inserting nodes.<n>We propose a defense framework based on adversarial training to strengthen GNN-based NIDS against structural attacks.
  arXiv  Detail & Related papers  (2026-08-25T12:06:12Z)
• Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach [0.20443308797642964]
  New wave of adversarial attacks makes Network Intrusion Detection Systems more open to such threats.<n>State-the-art adversarial training methods have shown promising results in producing more robust robustnesss.<n>We present an insightful approach, called LARAR, that incorporates additional layer-wise vulnerability analysis and adaptive weighting in conventional adversarial training methods.
  arXiv  Detail & Related papers  (2026-05-09T12:10:01Z)
• Adversarial generalization of unfolding (model-based) networks [0.0]
  We study the adversarial generalization of unfolding networks when perturbed with $l$-norm constrained attacks.<n>This is the first theoretical analysis on the adversarial generalization of unfolding networks.<n>We present a series of experiments on real-world data, with results corroborating our derived theory.
  arXiv  Detail & Related papers  (2025-09-18T19:17:07Z)
• Edge-Only Universal Adversarial Attacks in Distributed Learning [49.546479320670464]
  In this work, we explore the feasibility of generating universal adversarial attacks when an attacker has access to the edge part of the model only. Our approach shows that adversaries can induce effective mispredictions in the unknown cloud part by leveraging key features on the edge side. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part.
  arXiv  Detail & Related papers  (2024-11-15T11:06:24Z)
• Efficient Network Representation for GNN-based Intrusion Detection [2.321323878201932]
  The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages. We propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task. We present a Graph Neural Network (GNN) based framework responsible for exploiting the proposed graph structure.
  arXiv  Detail & Related papers  (2023-09-11T16:10:12Z)
• Poisoning Network Flow Classifiers [10.055241826257083]
  This paper focuses on poisoning attacks, specifically backdoor attacks, against network traffic flow classifiers. We investigate the challenging scenario of clean-label poisoning where the adversary's capabilities are constrained to tampering only with the training data. We describe a trigger crafting strategy that leverages model interpretability techniques to generate trigger patterns that are effective even at very low poisoning rates.
  arXiv  Detail & Related papers  (2023-06-02T16:24:15Z)
• Stimulative Training++: Go Beyond The Performance Limits of Residual Networks [91.5381301894899]
  Residual networks have shown great success and become indispensable in recent deep neural network models. Previous research has suggested that residual networks can be considered as ensembles of shallow networks. We identify a problem that is analogous to social loafing, whereworks within a residual network are prone to exert less effort when working as part of a group compared to working alone.
  arXiv  Detail & Related papers  (2023-05-04T02:38:11Z)
• A comparative study of attention mechanism and generative adversarial network in facade damage segmentation [0.0]
  Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation. This paper adopts U-net, a representative convolutional neural network, as the primary network and presents a comparative study in two steps.
  arXiv  Detail & Related papers  (2022-09-27T09:59:32Z)
• A Systematic Evaluation of Node Embedding Robustness [77.29026280120277]
  We assess the empirical robustness of node embedding models to random and adversarial poisoning attacks. We compare edge addition, deletion and rewiring strategies computed using network properties as well as node labels. We found that node classification suffers from higher performance degradation as opposed to network reconstruction.
  arXiv  Detail & Related papers  (2022-09-16T17:20:23Z)
• Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity [3.8673567847548114]
  Adversarial examples can easily degrade the classification performance in neural networks. This work proposes an incremental dissipativity-based robustness certificate for neural networks.
  arXiv  Detail & Related papers  (2021-11-25T04:42:57Z)
• Defensive Tensorization [113.96183766922393]
  We propose tensor defensiveization, an adversarial defence technique that leverages a latent high-order factorization of the network. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio task and binary networks.
  arXiv  Detail & Related papers  (2021-10-26T17:00:16Z)
• Attribution Preservation in Network Compression for Reliable Network Interpretation [81.84564694303397]
  Neural networks embedded in safety-sensitive applications rely on input attribution for hindsight analysis and network compression to reduce its size for edge-computing. We show that these seemingly unrelated techniques conflict with each other as network compression deforms the produced attributions. This phenomenon arises due to the fact that conventional network compression methods only preserve the predictions of the network while ignoring the quality of the attributions.
  arXiv  Detail & Related papers  (2020-10-28T16:02:31Z)
• Molecule Property Prediction and Classification with Graph Hypernetworks [113.38181979662288]
  We show that the replacement of the underlying networks with hypernetworks leads to a boost in performance. A major difficulty in the application of hypernetworks is their lack of stability. A recent work has tackled the training instability of hypernetworks in the context of error correcting codes.
  arXiv  Detail & Related papers  (2020-02-01T16:44:34Z)

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.