Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid

2021 ◽  
Author(s):  
Andrea Ceschini ◽  
Antonello Rosato ◽  
Federico Succetti ◽  
Francesco Di Luzio ◽  
Massimo Mitolo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Deep Neural Networks ◽  
Electric Energy ◽  
Distribution Grid ◽  
Energy Theft

scholarly journals Enhanced Network Anomaly Detection Based on Deep Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 48231-48246 ◽  
Author(s):  
Sheraz Naseer ◽  
Yasir Saleem ◽  
Shehzad Khalid ◽  
Muhammad Khawar Bashir ◽  
Jihun Han ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Deep Neural Networks ◽  
Network Anomaly Detection

scholarly journals Model fusion of deep neural networks for anomaly detection

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nouar AlDahoul ◽  
Hezerul Abdul Karim ◽  
Abdulaziz Saleh Ba Wazir
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Denial Of Service ◽  
Traffic Data ◽  
Model Fusion ◽  
Class Weight ◽  
Network Anomaly Detection

AbstractNetwork Anomaly Detection is still an open challenging task that aims to detect anomalous network traffic for security purposes. Usually, the network traffic data are large-scale and imbalanced. Additionally, they have noisy labels. This paper addresses the previous challenges and utilizes million-scale and highly imbalanced ZYELL’s dataset. We propose to train deep neural networks with class weight optimization to learn complex patterns from rare anomalies observed from the traffic data. This paper proposes a novel model fusion that combines two deep neural networks including binary normal/attack classifier and multi-attacks classifier. The proposed solution can detect various network attacks such as Distributed Denial of Service (DDOS), IP probing, PORT probing, and Network Mapper (NMAP) probing. The experiments conducted on a ZYELL’s real-world dataset show promising performance. It was found that the proposed approach outperformed the baseline model in terms of average macro Fβ score and false alarm rate by 17% and 5.3%, respectively.

scholarly journals RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

2021 ◽  
Author(s):  
Boyang Liu ◽  
Ding Wang ◽  
Kaixiang Lin ◽  
Pang-Ning Tan ◽  
Jiayu Zhou
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Missing Values ◽  
Deep Neural Networks ◽  
Reconstruction Error ◽  
Feature Representation ◽  
High Complexity ◽  
Unsupervised Anomaly Detection ◽  
Robust Framework

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect the anomalies. However, due to the high complexity brought upon by the over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.

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