Support Vector Based Anomaly Detection in Federated Learning

• URL: http://arxiv.org/abs/2407.03920v1
• Date: Thu, 4 Jul 2024 13:32:17 GMT
• Title: Support Vector Based Anomaly Detection in Federated Learning
• Authors: Massimo Frasson, Dario Malchiodi,
• Abstract summary: Anomaly detection plays a crucial role in various domains, from cybersecurity to industrial systems. Traditional centralized approaches often encounter challenges related to data privacy. This work introduces two innovative algorithms for anomaly detection in a federated setting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection plays a crucial role in various domains, from cybersecurity to industrial systems. However, traditional centralized approaches often encounter challenges related to data privacy. In this context, Federated Learning emerges as a promising solution. This work introduces two innovative algorithms--Ensemble SVDD and Support Vector Election--that leverage Support Vector Machines for anomaly detection in a federated setting. In comparison with the Neural Networks typically used in within Federated Learning, these new algorithms emerge as potential alternatives, as they can operate effectively with small datasets and incur lower computational costs. The novel algorithms are tested in various distributed system configurations, yielding promising initial results that pave the way for further investigation.

Related papers

• Faster Predictive Coding Networks via Better Initialization [52.419343840654186]
  We propose a new technique for predictive coding networks that aims to preserve the iterative progress made on previous training samples.<n>Our experiments demonstrate substantial improvements in convergence speed and final test loss in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2026-01-28T08:52:19Z)
• Algorithms for Adversarially Robust Deep Learning [58.656107500646364]
  We discuss recent progress toward designing algorithms that exhibit desirable robustness properties.<n>We present new algorithms that achieve state-of-the-art generalization in medical imaging, molecular identification, and image classification.<n>We propose new attacks and defenses, which represent the frontier of progress toward designing robust language-based agents.
  arXiv  Detail & Related papers  (2025-09-23T14:48:58Z)
• Deep Anomaly Detection in Text [3.4265828682659705]
  This thesis aims to develop a method for detecting anomalies by exploiting pretext tasks tailored for text corpora. This approach greatly improves the state-of-the-art on two datasets, 20Newsgroups, and AG News, for both semi-supervised and unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2023-12-14T22:04:43Z)
• Intrusion Detection: A Deep Learning Approach [0.0]
  The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored.
  arXiv  Detail & Related papers  (2023-06-13T07:58:40Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• GowFed -- A novel Federated Network Intrusion Detection System [0.15469452301122172]
  This work presents GowFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GowFed have been developed based on state-of the-art knowledge: (1) a vanilla version; and (2) a version instrumented with an attention mechanism. Overall, GowFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.
  arXiv  Detail & Related papers  (2022-10-28T23:53:37Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison [0.6299766708197883]
  Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. We propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture by identifying dissimilarities between specific neurons amongst the clients.
  arXiv  Detail & Related papers  (2021-10-19T19:43:28Z)
• A Novel Open Set Energy-based Flow Classifier for Network Intrusion Detection [0.0]
  We propose an adaptation of single-class Energy Flow (EFC) algorithm to be used in Network Intrusion Detection Systems (NIDS)<n>EFC correctly classifies not only known attacks, but also unknown ones, and differs from other proposals from the literature by presenting a single layer with low temporal complexity.
  arXiv  Detail & Related papers  (2021-09-23T09:13:33Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• A cognitive based Intrusion detection system [0.0]
  Intrusion detection is one of the important mechanisms that provide computer networks security. This paper proposes a new approach based on Deep Neural Network ans Support vector machine classifier. The proposed model predicts the attacks with better accuracy for intrusion detection rather similar methods.
  arXiv  Detail & Related papers  (2020-05-19T13:30:30Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)

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.