Enhancing Resiliency of Sketch-based Security via LSB Sharing-based Dynamic Late Merging

• URL: http://arxiv.org/abs/2503.11777v1
• Date: Fri, 14 Mar 2025 18:12:14 GMT
• Title: Enhancing Resiliency of Sketch-based Security via LSB Sharing-based Dynamic Late Merging
• Authors: Seungsam Yang, Seyed Mohammad Mehdi Mirnajafizadeh, Sian Kim, Rhongho Jang, DaeHun Nyang,
• Abstract summary: We introduce a new sketch-oriented attack, which threatens a stream of state-of-the-art sketches and their security applications.<n>Siamese Counter delivers 47% accurate results than a state-of-the-art scheme, and demonstrates up to 82% more accurate estimation under normal measurement scenarios.
• Score: 6.601355678995729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the exponentially growing Internet traffic, sketch data structure with a probabilistic algorithm has been expected to be an alternative solution for non-compromised (non-selective) security monitoring. While facilitating counting within a confined memory space, the sketch's memory efficiency and accuracy were further pushed to their limit through finer-grained and dynamic control of constrained memory space to adapt to the data stream's inherent skewness (i.e., Zipf distribution), namely small counters with extensions. In this paper, we unveil a vulnerable factor of the small counter design by introducing a new sketch-oriented attack, which threatens a stream of state-of-the-art sketches and their security applications. With the root cause analyses, we propose Siamese Counter with enhanced adversarial resiliency and verified feasibility with extensive experimental and theoretical analyses. Under a sketch pollution attack, Siamese Counter delivers 47% accurate results than a state-of-the-art scheme, and demonstrates up to 82% more accurate estimation under normal measurement scenarios.

Related papers

• A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction [42.71119573226285]
  The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors.<n>We introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning.<n> Experimental evaluation confirms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm.
  arXiv  Detail & Related papers  (2025-07-13T05:31:35Z)
• Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning [54.26807397329468]
  This work explores a previously overlooked vulnerability in distributed deep learning systems.<n>An adversary who intercepts the intermediate features transmitted between them can still pose a serious threat.<n>We propose an exploitation strategy specifically designed for distributed settings.
  arXiv  Detail & Related papers  (2025-07-09T20:09:00Z)
• Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel Attacks [6.373405051241682]
  We propose a novel approach for performing side-channel attacks on elliptic curve cryptography. We adopt a long-short-term memory (LSTM) neural network to analyze a power trace and identify patterns of operation. We show that current countermeasures, specifically the coordinate randomization technique, are not sufficient to protect against side channels.
  arXiv  Detail & Related papers  (2025-02-24T17:02:40Z)
• How Breakable Is Privacy: Probing and Resisting Model Inversion Attacks in Collaborative Inference [9.092229145160763]
  Collaborative inference improves computational efficiency for edge devices by transmitting intermediate features to cloud models.<n>There is no established criterion for assessing model inversion attacks (MIAs)<n>We propose SiftFunnel, a privacy-preserving framework to resist MIA while maintaining usability.
  arXiv  Detail & Related papers  (2025-01-01T13:00:01Z)
• Certifiably Byzantine-Robust Federated Conformal Prediction [49.23374238798428]
  We introduce a novel framework Rob-FCP, which executes robust federated conformal prediction effectively countering malicious clients. We empirically demonstrate the robustness of Rob-FCP against diverse proportions of malicious clients under a variety of Byzantine attacks.
  arXiv  Detail & Related papers  (2024-06-04T04:43:30Z)
• Disparate Impact on Group Accuracy of Linearization for Private Inference [48.27026603581436]
  We show that reducing the number of ReLU activations disproportionately decreases the accuracy for minority groups compared to majority groups. We also show how a simple procedure altering the fine-tuning step for linearized models can serve as an effective mitigation strategy.
  arXiv  Detail & Related papers  (2024-02-06T01:56:29Z)
• Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting [11.185176107646956]
  Power system load data can inadvertently reveal the daily routines of residential users, posing a risk to their property security. We introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-02T16:39:08Z)
• Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous [8.191688622709444]
  We propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes. The proposed adversarial attack detector achieves a detection accuracy of 99.21%.
  arXiv  Detail & Related papers  (2023-11-10T11:07:31Z)
• Lightweight, Uncertainty-Aware Conformalized Visual Odometry [2.429910016019183]
  Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics. Emerging edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties. This paper presents a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands.
  arXiv  Detail & Related papers  (2023-03-03T20:37:55Z)
• A Secure Federated Learning Framework for Residential Short Term Load Forecasting [1.1254693939127909]
  Federated Learning (FL) is a machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. We develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture.
  arXiv  Detail & Related papers  (2022-09-29T04:36:16Z)
• Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds [54.51566432934556]
  We consider distributed optimization methods for problems where forming the Hessian is computationally challenging. We leverage randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems.
  arXiv  Detail & Related papers  (2022-03-18T05:49:13Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)
• Unsupervised Scale-consistent Depth Learning from Video [131.3074342883371]
  We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training. Thanks to the capability of scale-consistent prediction, we show that our monocular-trained deep networks are readily integrated into the ORB-SLAM2 system. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training.
  arXiv  Detail & Related papers  (2021-05-25T02:17:56Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

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.