Randomization-Based Intrusion Detection System for Advanced Metering Infrastructure*

2015 ◽  
Vol 18 (2) ◽  
pp. 1-30 ◽  
Author(s):  
Muhammad Qasim Ali ◽  
Ehab Al-Shaer
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Advanced Metering Infrastructure ◽  
Advanced Metering

scholarly journals A framework for intrusion detection system in advanced metering infrastructure

2012 ◽  
Vol 7 (1) ◽  
pp. 195-205 ◽  
Author(s):  
Nasim Beigi Mohammadi ◽  
Jelena Mišić ◽  
Vojislav B. Mišić ◽  
Hamzeh Khazaei
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Advanced Metering Infrastructure ◽  
Advanced Metering

scholarly journals Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 626
Author(s):  
Ruizhe Yao ◽  
Ning Wang ◽  
Zhihui Liu ◽  
Peng Chen ◽  
Xianjun Sheng
Keyword(s):  
Intrusion Detection ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Detection System ◽  
Memory Function ◽  
Cross Layer ◽  
Advanced Metering Infrastructure ◽  
Global Features ◽  
Network Intrusion ◽  
Advanced Metering

Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.

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