Enhancing Security and Energy Efficiency in Smart Energy Management Systems through IoT Device Detection and Machine Learning Techniques
This research endeavors to fortify the security aspects of smart energy management systems (SEMSs) while optimizing energy efficiency by amalgamating Internet of Things (IoT) technologies. By integrating the IoT into the SEMS framework, this study aims to explore and enhance cybersecurity measures b...
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| Published in: | 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP) Vol. 1; pp. 506 - 511 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
11-07-2024
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| Abstract | This research endeavors to fortify the security aspects of smart energy management systems (SEMSs) while optimizing energy efficiency by amalgamating Internet of Things (IoT) technologies. By integrating the IoT into the SEMS framework, this study aims to explore and enhance cybersecurity measures by implementing cutting-edge strategies to protect against potential vulnerabilities and threats. This comprehensive approach involves leveraging IoT devices to create a secure and efficient ecosystem within an SEMS, encompassing encryption techniques, authentication protocols, anomaly detection, and other innovative security strategies to ensure the resilience and optimal performance of these interconnected energy management systems. |
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| AbstractList | This research endeavors to fortify the security aspects of smart energy management systems (SEMSs) while optimizing energy efficiency by amalgamating Internet of Things (IoT) technologies. By integrating the IoT into the SEMS framework, this study aims to explore and enhance cybersecurity measures by implementing cutting-edge strategies to protect against potential vulnerabilities and threats. This comprehensive approach involves leveraging IoT devices to create a secure and efficient ecosystem within an SEMS, encompassing encryption techniques, authentication protocols, anomaly detection, and other innovative security strategies to ensure the resilience and optimal performance of these interconnected energy management systems. |
| Author | Algarni, Mohammed Ali Sakly, Houneida Kraiem, Naoufel |
| Author_xml | – sequence: 1 givenname: Mohammed Ali surname: Algarni fullname: Algarni, Mohammed Ali organization: King Khalid University,College of Computer Science,Abha,Saudi Arabia,61413 – sequence: 2 givenname: Naoufel surname: Kraiem fullname: Kraiem, Naoufel organization: ENSI, Manouba University, Campus Universitaire de la Manouba,RIADI Laboratory,La Manouba,Tunisia,2010 – sequence: 3 givenname: Houneida surname: Sakly fullname: Sakly, Houneida organization: Sousse,Technopole, Sahloul,Center for Research on Microelectronics and Nanotechnology (CRMN),Sousse,Tunisia |
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| Snippet | This research endeavors to fortify the security aspects of smart energy management systems (SEMSs) while optimizing energy efficiency by amalgamating Internet... |
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| StartPage | 506 |
| SubjectTerms | Cybersecurity Device detection Ecosystems Encryption Energy efficiency Image processing Internet of Things (IoT) Machine learning Performance evaluation Protocols Security Smart energy management systems (SEMSs) Vulnerabilities |
| Title | Enhancing Security and Energy Efficiency in Smart Energy Management Systems through IoT Device Detection and Machine Learning Techniques |
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