Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains

• URL: http://arxiv.org/abs/2210.04088v1
• Date: Sat, 8 Oct 2022 18:52:43 GMT
• Title: Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains
• Authors: Mohammad Ismail Daud
• Abstract summary: We propose a novel system that aims to remedy the issues by examining deep textual patterns of network-oriented content. We also propose to use federated learning that allows users to take advantage of each other's localized knowledge/experience regarding what should or should not be blocked on a network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current content filtering and blocking methods are susceptible to various circumvention techniques and are relatively slow in dealing with new threats. This is due to these methods using shallow pattern recognition that is based on regular expression rules found in crowdsourced block lists. We propose a novel system that aims to remedy the aforementioned issues by examining deep textual patterns of network-oriented content relating to the domain being interacted with. Moreover, we propose to use federated learning that allows users to take advantage of each other's localized knowledge/experience regarding what should or should not be blocked on a network without compromising privacy. Our experiments show the promise of our proposed approach in real world settings. We also provide data-driven recommendations on how to best implement the proposed system.

Related papers

• Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization [60.899082019130766]
  We introduce a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN.
  arXiv  Detail & Related papers  (2024-07-23T15:07:52Z)
• Towards Universal Dense Blocking for Entity Resolution [49.06313308481536]
  We propose UniBlocker, a dense blocker that is pre-trained on a domain-independent, easily-obtainable corpus. By conducting domain-independent pre-training, UniBlocker can be adapted to various downstream blocking scenarios without requiring domain-specific fine-tuning. Our experiments show that the proposed UniBlocker, without any domain-specific learning, significantly outperforms previous self- and unsupervised dense blocking methods.
  arXiv  Detail & Related papers  (2024-04-23T08:39:29Z)
• Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things [3.280202415151067]
  Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone. We propose an effective non-parametric approach FedRR to address the adversarial attack problem. Our proposed method is capable of accurately detecting adversarial clients and controlling the false alarm rate under the scenario with no attack occurring.
  arXiv  Detail & Related papers  (2023-10-02T18:25:02Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Vertical Federated Linear Contextual Bandits [36.40993993623568]
  We design a customized encryption scheme named matrix-based mask mechanism(O3M) for encrypting local contextual information. We apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation.
  arXiv  Detail & Related papers  (2022-10-20T06:59:42Z)
• Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search [61.24539128142504]
  Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. Most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities. We propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels.
  arXiv  Detail & Related papers  (2022-08-30T16:14:18Z)
• A cross-domain recommender system using deep coupled autoencoders [77.86290991564829]
  Two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors.
  arXiv  Detail & Related papers  (2021-12-08T15:14:26Z)
• Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation [1.2074552857379273]
  Cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning.
  arXiv  Detail & Related papers  (2021-10-25T05:17:58Z)
• Application of the Multi-label Residual Convolutional Neural Network text classifier using Content-Based Routing process [0.0]
  We will present an NLP application in text classifying process using the content-based router. The ultimate goal throughout this article is to predict the event described by a legal ad from the plain text of the ad.
  arXiv  Detail & Related papers  (2021-10-19T19:10:34Z)
• Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond [97.25179345878443]
  This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB) AWB can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels. Experiments demonstrate that the proposed method achieves state-of-the-art performance with significant improvements on multiple UDA person re-identification tasks.
  arXiv  Detail & Related papers  (2020-06-11T15:40:40Z)

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.