A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis

• URL: http://arxiv.org/abs/2204.07726v1
• Date: Sat, 16 Apr 2022 05:33:01 GMT
• Title: A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis
• Authors: Lingzi Kong, Daoqi Han, Junmei Ding, Mingrui Fan and Yueming Lu
• Abstract summary: We propose a hierarchical terminal recognition approach that applies the details of grid data. We have formed a two-level model structure by segmenting the grid data. Through the selection and reconstruction of features, we combine three algorithms to achieve accurate identification of terminal types.
• Score: 0.48298211429517085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognizing the type of connected devices to a network helps to perform security policies. In smart grids, identifying massive number of grid metering terminals based on network traffic analysis is almost blank and existing research has not proposed a targeted end-to-end model to solve the flow classification problem. Therefore, we proposed a hierarchical terminal recognition approach that applies the details of grid data. We have formed a two-level model structure by segmenting the grid data, which uses the statistical characteristics of network traffic and the specific behavior characteristics of grid metering terminals. Moreover, through the selection and reconstruction of features, we combine three algorithms to achieve accurate identification of terminal types that transmit network traffic. We conduct extensive experiments on a real dataset containing three types of grid metering terminals, and the results show that our research has improved performance compared to common recognition models. The combination of an autoencoder, K-Means and GradientBoost algorithm achieved the best recognition rate with F1 value of 98.3%.

Related papers

• Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
  We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• Traffic Light Recognition using Convolutional Neural Networks: A Survey [4.451479907610764]
  We conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs) Based on an underlying architecture, we cluster methods into three major groups. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.
  arXiv  Detail & Related papers  (2023-09-05T11:50:38Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network [55.21501819988941]
  Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. We propose an end-to-end network traffic classification method.
  arXiv  Detail & Related papers  (2022-10-30T01:52:05Z)
• HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting [0.0]
  We study the heterogeneous characteristics inherent in traffic signal data to learn hidden relationships between sensors in various ways. We propose a network decentralization attention-aware graph convolution network (HAGCN) method that aggregates the hidden states of adjacent nodes.
  arXiv  Detail & Related papers  (2022-09-05T13:45:52Z)
• Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks [6.6147550436077776]
  This paper aims to discover whether Feature Reduction (FR) and Machine Learning (ML) techniques can be generalised across various datasets. The detection accuracy of three Feature Extraction (FE) algorithms; Principal Component Analysis (PCA), Auto-encoder (AE), and Linear Discriminant Analysis (LDA) is evaluated.
  arXiv  Detail & Related papers  (2021-08-28T23:52:18Z)
• Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder [12.194597074511863]
  A method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The presented method outperforms other state-of-the-art outlier detection approaches.
  arXiv  Detail & Related papers  (2021-05-05T08:24:03Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)
• Characterization and Identification of Cloudified Mobile Network Performance Bottlenecks [0.0]
  This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.
  arXiv  Detail & Related papers  (2020-07-22T14:46:51Z)

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.