EGSP: Enhanced Grid Sensor Placement Algorithm for Energy Theft Detection in Smart Grids

Patil, Yuvaraj S.; Sankpal, S. V.

doi:10.1109/i2ct45611.2019.9033759

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…In 2020, Chen et al [9] developed a network intrusion detection system based on a CNN-based data collection and monitoring control system to safeguard the Industrial Internet of Things from DDoS attacks and other cyberattacks, cyberassaults against SCADA systems in general and cyberattacks on SCADA systems in particular. Patil and Sankpal [10] investigated power theft attacks in intelligent grids in 2019 and developed a deep learning-based intrusion detection system to identify such cyberassaults. In 2021, Yuan et al [11]…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning

Tharewal

Ashfaque

Banu

et al. 2022

Wireless Communications and Mobile Computing

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The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision-making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large-scale original input data into higher-level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial-and-error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near-end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision-making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL-IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning

Tharewal

Ashfaque

Banu

et al. 2022

Wireless Communications and Mobile Computing

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“…Patil and Sankpal in [14] proposes an enhanced grid sensor placement (EGSP) algorithm to place grid sensors in the distribution network to monitor and control the smart meters installed in the field. The algorithm provides a simple and efficient way to place grid sensors in the distribution network for monitoring and controlling the smart meters deployed in the distribution network.…”

Section: Related Workmentioning

confidence: 99%

Malicious Node Detection in Smart Grid Networks

Yahmadi¹,

Ahmed²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.

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Convolution Neural Network Scheme for Detection of Electricity Theft in Smart Grids

Palmer

Kathrine

Jebapriya

2022

Machine Learning Techniques for Smart City Applications: Trends and Solutions

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EGSP: Enhanced Grid Sensor Placement Algorithm for Energy Theft Detection in Smart Grids

Cited by 5 publications

References 7 publications

Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning

Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning

Malicious Node Detection in Smart Grid Networks

Convolution Neural Network Scheme for Detection of Electricity Theft in Smart Grids

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