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Anomaly Detection to Counter DDoS Attacks on Smart Electric Meter Systems

Mohammed. I. Ibrahim, Abeer Salawi, Salwa. H. Alghamdi, Nada Alkenani, Amani Almuntashiri, Rawan Alghamdi, Yusra Abdullah, Amal Ali, Wejdan Ahmed Alghamdi, Maram Alkhayyal
2022 International Journal of Cryptocurrency Research  
Bidirectional communication infrastructure of smart systems, such as, smart grids, are vulnerable to network attacks like Distributed Denial of Services (DDoS) and can be a major concern in the present  ...  In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real-life smart city platform.  ...  We called the deep CNN part of the proposed model, Deep Convolutional Neural Network (DCNN).  ... 
doi:10.51483/ijccr.2.1.2022.12-18 fatcat:yfxp7k6sdrae7gnw7ddnu7d2jq

Cyber-Security Audit for Smart Grid Networks: An Optimized Detection Technique Based on Bayesian Deep Learning

Alexander N. Ndife, Yodthong Mensin, Wattanapong Rakwichian, Paisarn Muneesawang
2022 Journal of Internet Services and Information Security  
This paper discusses a proposed Bayesian Neural Networks for time-series TCP/IP packets intrusion detection and threats classification in a grid network.  ...  Security of computers, networks and their communication protocols are vital in smart grid technology operation and its management.  ...  However, the dynamics of cyber-attacks on smart grids creates attack patterns have complex characteristics.  ... 
doi:10.22667/jisis.2022.05.31.095 dblp:journals/jisis/NdifeMRM22 fatcat:hh27byggfrfkffw3ietr4kiifq

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

Jianguo Ding, Attia Qammar, Zhimin Zhang, Ahmad Karim, Huansheng Ning
2022 Energies  
Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure.  ...  Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed.  ...  However, in Table 11 , multiple deep learning algorithms such as Recurrent Neural Networks (RNN), Artificial Neural Network (ANN), Deep Neural Network (DNN), etc., have been implemented in the literature  ... 
doi:10.3390/en15186799 fatcat:wug4ifj2f5hz3fj322vnsodquu

An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids [article]

Hossein Mohammadi Rouzbahani, Hadis Karimipour, Lei Lei
2021 arXiv   pre-print
In this paper, an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed.  ...  However, using these infrastructures make smart grids more vulnerable to cyber threats especially electricity theft.  ...  ENSEMBLE DEEP CONVOLUTIONAL NEURAL NETWORK (EDCNN) ALGORITHM An anti-social behavior like electricity theft can cause serious problems in smart grids.  ... 
arXiv:2102.06039v1 fatcat:76tin3xdi5fdnm6rkr3wmy4akq

Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Mohamed Amine Ferrag, Lei Shu, Hamouda Djallel, Kim-Kwang Raymond Choo
2021 Electronics  
In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks  ...  Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting  ...  Specifically, the deep neural network model achieves a recall of 99% in binary classification compared to 83% and 62% in multiclass classification.  ... 
doi:10.3390/electronics10111257 fatcat:xojjd57fwrhdjgy67u5ne2roum

A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions [article]

Tala Talaei Khoei, Hadjar Ould Slimane, Naima Kaabouch
2022 arXiv   pre-print
In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication  ...  One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges.  ...  cyber-attack classifications in smart grid.  ... 
arXiv:2207.07738v1 fatcat:px2nhhvasngvfiuoe4ncty2eu4

A Review on Conceptual Model of Cyber Attack Detection and Mitigation Using Deep Ensemble Model

Sangeetha Prabhu, Nethravathi P. S.
2022 International journal of applied engineering and management letters  
When the model identifies the attacker node, it is removed via the BAIT technique from the network.  ...  An Intrusion Detection System is critical for identifying and blocking attacks in IoT networks.  ...  Synthetic neural networks, for example, are used in defect detection in power and smart grid systems because they are adaptable systems inspired by organic systems.  ... 
doi:10.47992/ijaeml.2581.7000.0126 fatcat:l5nslzedcre4bcu2lskckyihda

IEEE Access Special Section: Security Analytics and Intelligence for Cyber Physical Systems

Haider Abbas, Hiroki Suguri, Zheng Yan, William Allen, Xiali Sharon Zhang
2020 IEEE Access  
In the article ''A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids,'' Wang et al. developed a data analytical approach based on a data-centric paradigm  ...  Units (RTUs) and Control Center (CC) in the smart grid through SEDEA.  ... 
doi:10.1109/access.2020.3036713 fatcat:mmmaqfaovfhtlmms7iqxykxnjq

2021 Index IEEE Transactions on Smart Grid Vol. 12

2021 IEEE Transactions on Smart Grid  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, TSG Nov. 2021 4641-4654 Feedforward neural networks Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids.  ... 
doi:10.1109/tsg.2021.3137570 fatcat:xjssgbcfnrcvzf4qqqwifu6e3u

Towards Accurate and Efficient Classification of Power System Contingencies and Cyber-attacks Using Recurrent Neural Networks

Wei-Chih Hong, Ding-Ray Huang, Chih-Lung Chen, Jung-San Lee
2020 IEEE Access  
INDEX TERMS Intrusion detection, recurrent neural networks, smart grids, phasor measurement units.  ...  This paper presents the results of applying deep learning techniques on open datasets recorded from a power system testbed to classify contingencies and cyber-attacks.  ...  Accurate and efficient classification of the contingencies and cyber-attacks will play an important part in the reliability of the ADMS and the smart grid.  ... 
doi:10.1109/access.2020.3007609 fatcat:j3iyhgxiubelbgrzflfolhozg4

Toward a Deep Learning-Driven Intrusion Detection Approach for Internet of Things [article]

Mengmeng Ge, Naeem Firdous Syed, Xiping Fu, Zubair Baig, Antonio Robles-Kelly
2020 arXiv   pre-print
In this paper, we present a novel intrusion detection approach for IoT networks through the application of a deep learning technique.  ...  However, due to the limitation of constrained resources and computational capabilities, IoT networks are prone to various cyber attacks.  ...  In this work, we propose a novel intrusion detection approach against cyber attacks for IoT networks based upon the concept of deep learning.  ... 
arXiv:2007.09342v1 fatcat:m6jttbvoyrbwxiupwargkbihve

A Deep Learning Based Method for False Data Injection Attack Detection in AC Smart Islands

Moslem Dehghani, Abdollah Kavousifard, Morteza Dabaghjamanesh, Omid Avatefipour
2020 IET Generation, Transmission & Distribution  
This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island.  ...  This cyberprotection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending  ...  Choosing appropriate variables with the aim of implementing the neural networks in an efficient way is vital.  ... 
doi:10.1049/iet-gtd.2020.0391 fatcat:725tgewyuvfc3jr4hrtk25qfiy

Learning Multilevel Auto-encoders for DDoS Attack Detection in Smart Grid Network

Shan Ali, YuanCheng Li
2019 IEEE Access  
INDEX TERMS Auto-encoder, cyber security, DDoS attack detection, multiple kernel learning, smart grid.  ...  Bidirectional communication infrastructure of smart systems, such as smart grids, are vulnerable to network attacks like distributed denial of services (DDoS) and can be a major concern in the present  ...  A brief look at the literature on DDoS attack detection in the smart grid network reveals that the best performing methods include the ones that use neural network-based modeling strategies.  ... 
doi:10.1109/access.2019.2933304 fatcat:ivr2nttzcbhjdbsfixp5wkirgu

Table of Contents

2021 IEEE Transactions on Industrial Informatics  
Yoo 6925 SPECIAL SECTION ON DEEP LEARNING AND DATA ANALYTICS TO SUPPORT THE SMART GRID OPERATION WITH RENEWABLE ENERGY Guest Editorial: Deep Learning and Data Analytics to Support the Smart Grid Operation  ...  Wang 6906 Smart Collaborative Balancing for Dependable Network Components in Cyber-Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tii.2021.3092213 fatcat:y5utlj44oncrdgmwoqgobqozie

An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System

Abdulrahman Al-Abassi, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi
2020 IEEE Access  
The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations.  ...  INDEX TERMS Cyber-attacks, critical infrastructure, industrial control system, integrity attack, operation technology, information technology, deep learning, neural network.  ...  Then, new representations from each SAE are passed to a Deep Neural Network (DNN) via super vector and concatenated using a fusion activation vector.  ... 
doi:10.1109/access.2020.2992249 fatcat:vtudysg4z5fe5f3e2ppylysx7a
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