MetaDetect: Metamorphic Testing Based Anomaly Detection for Multi-UAV Wireless Networks

• URL: http://arxiv.org/abs/2312.04747v1
• Date: Thu, 7 Dec 2023 23:24:58 GMT
• Title: MetaDetect: Metamorphic Testing Based Anomaly Detection for Multi-UAV Wireless Networks
• Authors: Boyang Yan
• Abstract summary: The reliability of wireless Ad Hoc Networks (WANET) communication is much lower than wired networks. The proposed MT detection method is helpful for automatically identifying incidents/accident events on WANET.
• Score: 0.5076419064097734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliability of wireless Ad Hoc Networks (WANET) communication is much lower than wired networks. WANET will be impacted by node overload, routing protocol, weather, obstacle blockage, and many other factors, all those anomalies cannot be avoided. Accurate prediction of the network entirely stopping in advance is essential after people could do networking re-routing or changing to different bands. In the present study, there are two primary goals. Firstly, design anomaly events detection patterns based on Metamorphic Testing (MT) methodology. Secondly, compare the performance of evaluation metrics, such as Transfer Rate, Occupancy rate, and the Number of packets received. Compared to other studies, the most significant advantage of mathematical interpretability, as well as not requiring dependence on physical environmental information, only relies on the networking physical layer and Mac layer data. The analysis of the results demonstrates that the proposed MT detection method is helpful for automatically identifying incidents/accident events on WANET. The physical layer transfer Rate metric could get the best performance.

Related papers

• A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks [52.09243852066406]
  Adversarial Converging Time Score (ACTS) measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset.
  arXiv  Detail & Related papers  (2023-10-10T09:39:38Z)
• Deep Neural Networks based Meta-Learning for Network Intrusion Detection [0.24466725954625884]
  digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types. We propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection.
  arXiv  Detail & Related papers  (2023-02-18T18:00:05Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Cross-network transferable neural models for WLAN interference estimation [8.519313977400735]
  In this paper, we adopt a principled approach to interference estimation in robustnesss. We first use real data to characterize the factors that impact it, and derive a set of relevant synthetic workloads. We find, unsurprisingly, that Graph Conalvolution Networks (GCNs) yield the best performance overall.
  arXiv  Detail & Related papers  (2022-11-25T11:01:43Z)
• Big data analysis and distributed deep learning for next-generation intrusion detection system optimization [0.0]
  This paper proposes a solution to detect new threats with higher detection rate and lower false positive than already used IDS. We achieve those results by using Networking, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies.
  arXiv  Detail & Related papers  (2022-09-28T09:46:16Z)
• CNN-based Prediction of Network Robustness With Missing Edges [0.9239657838690227]
  We investigate the performance of CNN-based approaches for connectivity and controllability prediction, when partial network information is missing. A threshold is explored that if a total amount of more than 7.29% information is lost, the performance of CNN-based prediction will be significantly degenerated.
  arXiv  Detail & Related papers  (2022-08-25T03:36:20Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
  We show a simple NMS-free, end-to-end object detection framework. We attain on par or even improved detection accuracy compared with the original one-stage detector.
  arXiv  Detail & Related papers  (2021-01-28T02:38:29Z)
• EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection [2.223733768286313]
  We propose a new architecture to detect network attacks with minimal resources. The architecture is able to deal with either binary or multiclass classification problems and trades prediction speed for the accuracy of the network.
  arXiv  Detail & Related papers  (2020-07-27T11:31:37Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)

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.