Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Filters
























753 Hits in 7.7 sec

Reinforcement Learning for IoT Security: A Comprehensive Survey

Aashma Uprety, Danda B. Rawat
2020 IEEE Internet of Things Journal  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (306.3 kB)
https://web.archive.org/web/20210217114615/https://arxiv.org/pdf/2102.07247v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:

2021 pre-print arXiv:2102.07247v1 fatcat:wyclevr3bradhfkttgwc5ji45u
In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security  ...  Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system.  ...  The authors proposed a Q-learning based vulnerability analysis in a smart grid under sequential attack.  ... 
doi:10.1109/jiot.2020.3040957 fatcat:qtm2emhqmjhlncyczuij6nw3oa
Multiple Versions
Citation

Aashma Uprety, Danda B. Rawat. "Reinforcement Learning for IoT Security: A Comprehensive Survey." IEEE Internet of Things Journal 8.11 (2020) 1-1

Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey [article]

Nur Imtiazul Haque, Md Hasan Shahriar, Md Golam Dastgir, Anjan Debnath, Imtiaz Parvez, Arif Sarwat, Mohammad Ashiqur Rahman
2020 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (8.8 MB)
https://web.archive.org/web/20201009013402/https://arxiv.org/pdf/2010.00661v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point.  ...  Due to the promising computational and reasoning capability, machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators, respectively.  ...  in smart grid V.  ... 
arXiv:2010.00661v1 fatcat:lntkggxmi5gtlpumjz5ilwya34
Citation

Nur Imtiazul Haque, Md Hasan Shahriar, Md Golam Dastgir, Anjan Debnath, Imtiaz Parvez, Arif Sarwat, Mohammad Ashiqur Rahman. "Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey." arXiv (2020)

A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks

Jian Chen, Mohamed A. Mohamed, Udaya Dampage, Mostafa Rezaei, Saleh H. Salmen, Sami Al Obaid, Andres Annuk
2021 Applied Sciences  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (7.2 MB)
https://web.archive.org/web/20220503064623/https://mdpi-res.com/d_attachment/applsci/applsci-11-09972/article_deploy/applsci-11-09972-v2.pdf?version=1635321086

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts.  ...  The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data.  ...  Furthermore, the authors would like to thank the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant TK146, funded by the European Regional Development  ... 
doi:10.3390/app11219972 fatcat:tq3ix4iuxbd6jah45tralwdhxi
DOAJ
Citation

Jian Chen, Mohamed A. Mohamed, Udaya Dampage, Mostafa Rezaei, Saleh H. Salmen, Sami Al Obaid, Andres Annuk. "A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks." Applied Sciences 11.21 (2021) 9972

2020 Index IEEE Transactions on Smart Grid Vol. 11

2020 IEEE Transactions on Smart Grid  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (947.4 kB)
https://web.archive.org/web/20201219021524/https://ieeexplore.ieee.org/ielx7/5165411/9234085/09295476.pdf?tp=&arnumber=9295476&isnumber=9234085&ref=

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

., Small-Signal Stability Analysis and Active Damping Control of DC Microgrids Integrated With Distributed Electric Springs; 3737-3747 Hou, K., see Liu, X., TSG Nov. 2020 5431-5441 Hou, Y., see Liang  ...  Volt-VAR Control in Power Distribution Systems; TSG July 2020 3008-3018 Wang, W., see Gao, Y., TSG Nov. 2020 5357-5369 Wang, X., see Sheng, H., TSG Jan. 2020 95-105 Wang, X., Zhang, H., Shi, F., Wu, Q.  ...  ., +, TSG Nov. 2020 5184- 5192 Cyber-Attack Recovery Strategy for Smart Grid Based on Deep Reinforce- ment Learning.  ... 
doi:10.1109/tsg.2020.3044227 fatcat:qp5iogfnrnambc3qzuwvj4aega
Citation

"2020 Index IEEE Transactions on Smart Grid Vol. 11." IEEE Transactions on Smart Grid 11.6 (2020) 5471-5557

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning

Mariam Ibrahim, Ahmad Alsheikh, Ruba Elhafiz, Konstantinos Demertzis
2022 Computational Intelligence and Neuroscience  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (2.0 MB)
https://web.archive.org/web/20220617103138/https://downloads.hindawi.com/journals/cin/2022/2017366.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology  ...  Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR.  ...  Acknowledgments e authors would like to acknowledge Deanship of Graduate Studies and Scientific Research at the German Jordanian University for the Seed fund SATS 03/2020 and Eng.  ... 
doi:10.1155/2022/2017366 pmid:35432512 pmcid:PMC9010153 fatcat:ibzi6jqjdfatzkdxjadylcw4em
DOAJ
Citation

Mariam Ibrahim, Ahmad Alsheikh, Ruba Elhafiz, Konstantinos Demertzis. "Resiliency Assessment of Power Systems Using Deep Reinforcement Learning." Computational Intelligence and Neuroscience (2022) 1-10

Smart Grid Cyber-Physical Attack and Defense: A Review

Hang Zhang, Bo Liu, Hongyu Wu
2021 IEEE Access  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (6.9 MB)
https://web.archive.org/web/20210429075530/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09352761.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

Data-driven methods, especially machine learning based approaches, are an essential branch of cyber-physical attacks on the smart grid. Chen et al.  ...  attacks False Data Injection (FDI) attacks against state estimation, and bad data detection is one of the hottest topics in the smart grid.  ... 
doi:10.1109/access.2021.3058628 fatcat:5p2dbk6dlnbnplungfhv6k55lu
DOAJ
Citation

Hang Zhang, Bo Liu, Hongyu Wu. "Smart Grid Cyber-Physical Attack and Defense: A Review." IEEE Access (2021) 1-1

Review of Cyber-Physical Attacks in Smart Grids: A System-Theoretic Perspective

Francesco Liberati, Emanuele Garone, Alessandro Di Giorgio
2021 Electronics  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.1 MB)
https://web.archive.org/web/20210514055333/https://res.mdpi.com/d_attachment/electronics/electronics-10-01153/article_deploy/electronics-10-01153-v2.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL. The file type is application/pdf.

Then, a review of cyber-physical attacks on the smart grid is presented, starting from works on false data injection attacks against state estimation.  ...  The aim is to present a systematic and quantitative discussion of the basic working principles of the attacks, also in terms of the inner smart grid vulnerabilities and dynamical properties exploited by  ...  [98] proposes Q-Learning to find worst case sequential attacks (at each step, a positive reward is given to the Q-Learning agent if the current line interdiction results in disconnection of N lines  ... 
doi:10.3390/electronics10101153 fatcat:jjfvpp7t4zcirap75evxo6gnpq
DOAJ
Citation

Francesco Liberati, Emanuele Garone, Alessandro Di Giorgio. "Review of Cyber-Physical Attacks in Smart Grids: A System-Theoretic Perspective." Electronics 10.10 (2021) 1153

Inference of Tampered Smart Meters with Validations from Feeder-Level Power Injections [article]

Yachen Tang, Chee-Wooi Ten, Kevin P. Schneider
2019 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (516.7 kB)
https://web.archive.org/web/20200907084444/https://arxiv.org/pdf/1904.13208v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

The connection of the grid topology is illustrated as an adjacency or incidence matrix for the following analysis.  ...  The large-scale deployment of smart meters may potentially be tampered by malware by propagating their agents to other IP-based meters.  ...  The connection of the grid topology is illustrated as an adjacency or incidence matrix for the following analysis.  ... 
arXiv:1904.13208v1 fatcat:3eqtpq32nbcrxma7f3gfkck32m
Citation

Yachen Tang, Chee-Wooi Ten, Kevin P. Schneider. "Inference of Tampered Smart Meters with Validations from Feeder-Level Power Injections." arXiv (2019)

A Taxonomy of Data Attacks in Power Systems [article]

Sagnik Basumallik
2020 arXiv   pre-print

Preserved Fulltext

Web Archive Capture PDF (1.9 MB)
https://web.archive.org/web/20200321141505/https://arxiv.org/pdf/2002.11011v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

The goal is to provide a theoretically balanced approach to cyber attacks and their impacts on the reliable functioning of the electric grid.  ...  For each class, a comprehensive review of mathematical attack models is presented.  ...  Due to inherent non-convexity of lower level problem, a two-stage sequential approach was used instead of KKT based methods.  ... 
arXiv:2002.11011v1 fatcat:qnh3li5os5b4tn2iadhy7f24ze
Open Access
Citation

Sagnik Basumallik. "A Taxonomy of Data Attacks in Power Systems." arXiv (2020)

Integrated Clinical Environment Security Analysis Using Reinforcement Learning

Mariam Ibrahim, Ruba Elhafiz
2022 Bioengineering  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.5 MB)
https://web.archive.org/web/20220616234845/https://mdpi-res.com/d_attachment/bioengineering/bioengineering-09-00253/article_deploy/bioengineering-09-00253.pdf?version=1655109802

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

Therefore, this paper presents a Q-learning-based attack graph analysis approach in which an attack graph that is generated for the Integrated Clinical Environment system resembles the environment, and  ...  Q-learning can aid in determining the best route that the attacker can take in order to damage the system as much as possible with the least number of actions.  ...  A novel Q-learning-based vulnerability study of the electrical power grid in sequential topological attacks was described in [39] .  ... 
doi:10.3390/bioengineering9060253 pmid:35735496 pmcid:PMC9220416 fatcat:npryg6zzyvdnjf2iomnqjylnva
DOAJ
Citation

Mariam Ibrahim, Ruba Elhafiz. "Integrated Clinical Environment Security Analysis Using Reinforcement Learning." Bioengineering 9.6 (2022) 253

A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks

Li, Lu, Shi, Wang, Qiao, Liu
2019 Symmetry  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (5.0 MB)
https://web.archive.org/web/20200208063121/https://res.mdpi.com/d_attachment/symmetry/symmetry-11-00617/article_deploy/symmetry-11-00617-v2.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL. The file type is application/pdf.

Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm  ...  With the advent of software-defined radio (SDR), smart attacks that can flexibly select attack strategies according to the defender's state information are gradually attracting the attention of researchers  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym11050617 fatcat:7arl5urblrhqtoscbmykipqv7i
DOAJ Szczepanski
Citation

Li, Lu, Shi, Wang, Qiao, Liu. "A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks." Symmetry 11.5 (2019) 617

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  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (6.9 MB)
https://web.archive.org/web/20220719015126/http://isyou.info/jisis/vol12/no2/jisis-2022-vol12-no2-05.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

Security of computers, networks and their communication protocols are vital in smart grid technology operation and its management.  ...  Spatiotemporal feature engineering and uncertainty estimation in Bayesian modeling, were leveraged to learn novel attack features and classify attacks accordingly.  ...  The threat analysis process started with a mathematical framework for measurement of uncertainties in smart grid network based on Bayesian probability distribution.  ... 
doi:10.22667/jisis.2022.05.31.095 dblp:journals/jisis/NdifeMRM22 fatcat:hh27byggfrfkffw3ietr4kiifq
DOAJ
Citation

Alexander N. Ndife, Yodthong Mensin, Wattanapong Rakwichian, Paisarn Muneesawang. "Cyber-Security Audit for Smart Grid Networks: An Optimized Detection Technique Based on Bayesian Deep Learning." Journal of Internet Services and Information Security (2022) 95-114

2021 Index IEEE Transactions on Smart Grid Vol. 12

2021 IEEE Transactions on Smart Grid  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.0 MB)
https://web.archive.org/web/20220522080132/https://ieeexplore.ieee.org/ielx7/5165411/9582816/09664409.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Zhang, Q., +, TSG Sept. 2021 3889-Load distribution A Detection Mechanism Against Load-Redistribution Attacks in Smart Grids.  ...  ., +, TSG May 2021 2355-2364 Analysis of IoT-Based Load Altering Attacks Against Power Grids Using the Theory of Second-Order Dynamical Systems.  ... 
doi:10.1109/tsg.2021.3137570 fatcat:xjssgbcfnrcvzf4qqqwifu6e3u
Citation

"2021 Index IEEE Transactions on Smart Grid Vol. 12." IEEE Transactions on Smart Grid 12.6 (2021) 5483-5575

Models and Framework for Adversarial Attacks on Complex Adaptive Systems [article]

Vahid Behzadan, Arslan Munir
2017 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.2 MB)
https://web.archive.org/web/20191023115255/https://arxiv.org/pdf/1709.04137v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL. The file type is application/pdf.

Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies  ...  of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF.  ... 
arXiv:1709.04137v1 fatcat:risynvwcrffbddmogtwg5cmcli
Citation

Vahid Behzadan, Arslan Munir. "Models and Framework for Adversarial Attacks on Complex Adaptive Systems." arXiv (2017)

Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review [article]

Zhenyong Zhang, Mengxiang Liu, Mingyang Sun, Ruilong Deng, Peng Cheng, Dusit Niyato, Mo-Yuen Chow, Jiming Chen
2023 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (1.5 MB)
https://web.archive.org/web/20231229122043/https://arxiv.org/pdf/2308.15736v3.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL. The file type is application/pdf.

Other Versions

2023 pre-print
arXiv:2308.15736v1 fatcat:r7kaivgdnrcd7c3cugj3zjfrv4
2023 pre-print
arXiv:2308.15736v2 fatcat:jvximtlprvgojfpkctagol52wu
Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid.  ...  However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs).  ...  the fact that the smart grid is vulnerable to cyberattacks.  ... 
arXiv:2308.15736v3 fatcat:sie5kvavunh7zhborup5hjkd2u
Multiple Versions
Citation

Zhenyong Zhang, Mengxiang Liu, Mingyang Sun, Ruilong Deng, Peng Cheng, Dusit Niyato, Mo-Yuen Chow, Jiming Chen. "Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review." arXiv (2023)

« Previous Showing results 1 — 15 out of 753 results