A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks

• URL: http://arxiv.org/abs/2208.11887v1
• Date: Thu, 25 Aug 2022 06:39:29 GMT
• Title: A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
• Authors: Abhilash Singh, J. Amutha, Jaiprakash Nagar, Sandeep Sharma
• Abstract summary: Wireless Sensor Networks (WSNs) can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention.
• Score: 3.6748639131154315
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

Related papers

• MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs [3.887356044145916]
  Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. We propose an innovative intrusion detection approach that integrates Machine Learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs.
  arXiv  Detail & Related papers  (2024-02-17T18:04:08Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy [0.5310810820034502]
  Complex sensors such as LiDAR, RADAR, and event cameras have proliferated in autonomous robotics. These sensors are vulnerable to diverse failure mechanisms that can intricately interact with their operation environment. This paper introduces STARNet, a Sensor Trustworthiness and Anomaly Recognition Network designed to detect untrustworthy sensor streams.
  arXiv  Detail & Related papers  (2023-09-20T02:20:11Z)
• Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data [0.0]
  This study addresses the challenge of replacing dedicated axle detectors with a novel approach to real-time detection of train axles. The proposed Virtual Axle Detector with Enhanced Receptive Field (VADER) has been validated on a single-track railway bridge. Using raw data as input outperforms the state-of-the-art spectrogram-based method in both speed and memory usage by 99%.
  arXiv  Detail & Related papers  (2023-09-04T12:53:54Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [57.03126447713602]
  We present a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
  arXiv  Detail & Related papers  (2022-09-29T01:30:34Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Learning-based Localizability Estimation for Robust LiDAR Localization [13.298113481670038]
  LiDAR-based localization and mapping is one of the core components in many modern robotic systems. This work proposes a neural network-based estimation approach for detecting (non-)localizability during robot operation.
  arXiv  Detail & Related papers  (2022-03-11T01:12:00Z)
• Oriented R-CNN for Object Detection [61.78746189807462]
  This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN. In the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them.
  arXiv  Detail & Related papers  (2021-08-12T12:47:43Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks [62.997667081978825]
  We present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks. A set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel. The overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives.
  arXiv  Detail & Related papers  (2020-02-11T13:58:16Z)

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.