Electricity Theft Detection Model for Smart Meter Based on Residual Neural Network

2020 ◽  
Author(s):  
Yuan Chen ◽  
Gang Hua ◽  
Dingdong Feng ◽  
Haixiang Zang ◽  
Zhinong Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Smart Meter ◽  
Detection Model ◽  
Electricity Theft

scholarly journals Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shuan Li ◽  
Yinghua Han ◽  
Xu Yao ◽  
Song Yingchen ◽  
Jinkuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Power Grids ◽  
Detection Model ◽  
Electricity Theft ◽  
Utility Companies ◽  
Major Factors

As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.

scholarly journals A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 683 ◽  
Author(s):  
Gonzalo Farias ◽  
Ernesto Fabregas ◽  
Emmanuel Peralta ◽  
Héctor Vargas ◽  
Gabriel Hermosilla ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstacle Detection ◽  
Network Approach ◽  
Neural Network Approach ◽  
Detection Model

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

scholarly journals Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

2021 ◽  
Vol 7 (09) ◽  
pp. 1-6
Author(s):  
Adil Hussain Mohammed
Keyword(s):  
Neural Network ◽  
Feature Reduction ◽  
Training Dataset ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Detection Model ◽  
Network Training ◽  
Proposed Model ◽  
Trained Neural Network ◽  
Good Set

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.

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