MUD enabled deep learning framework for anomaly detection in IoT integrated smart building
Nowadays, many Internet of Things (IoT) devices of different types are used in creating smart applications like smart cities, smart industries, smart environments, and the applications of industry-4.0. IoT devices are used for different purposes, such as security, remote monitoring, resource allocat...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123000815 |
| _version_ | 1797674884537516032 |
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| author | Mirdula S Roopa M |
| author_facet | Mirdula S Roopa M |
| author_sort | Mirdula S |
| collection | DOAJ |
| description | Nowadays, many Internet of Things (IoT) devices of different types are used in creating smart applications like smart cities, smart industries, smart environments, and the applications of industry-4.0. IoT devices are used for different purposes, such as security, remote monitoring, resource allocation, threats, ecosystems, and vulnerabilities. This paper proposed a deep learning algorithm-based solution to tighten the security level in the IoT-Smart environment network. The Intrusion Detection System (IDS) considered in this paper is Network IDS, which investigates the manufacturer usage description, digital twins, and deep learning-based user behavior information. IoT devices' communication and the users in smart buildings are automatically connected in the Intelligent Communication system. Since many devices and users are interconnected in smart buildings, the probability of cyber-attack is high. Thus, better security is needed in smart buildings and smart environments. It should focus on securing IoT devices, users, and their communication. Hence, this paper developed a deep learning-based anomaly detection framework to dynamically monitor the issues and problems with MUD profiles and detect the anomaly behavior. The Manufacturer Usage Description (MUD) profiles, dynamic user behavior, IoT devices' traffic data the pattern of abnormal/anomaly traffic at the device level is predicted while traffic occurs. The MUD-ML-based model is implemented in Python software, verifying the results. |
| first_indexed | 2024-03-11T22:06:39Z |
| format | Article |
| id | doaj.art-240c4efa810d4b22a0fa6a22ab39165e |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-11T22:06:39Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-240c4efa810d4b22a0fa6a22ab39165e2023-09-25T04:12:37ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-09-015100186MUD enabled deep learning framework for anomaly detection in IoT integrated smart buildingMirdula S0Roopa M1Corresponding author.; Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu, IndiaDepartment of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu, IndiaNowadays, many Internet of Things (IoT) devices of different types are used in creating smart applications like smart cities, smart industries, smart environments, and the applications of industry-4.0. IoT devices are used for different purposes, such as security, remote monitoring, resource allocation, threats, ecosystems, and vulnerabilities. This paper proposed a deep learning algorithm-based solution to tighten the security level in the IoT-Smart environment network. The Intrusion Detection System (IDS) considered in this paper is Network IDS, which investigates the manufacturer usage description, digital twins, and deep learning-based user behavior information. IoT devices' communication and the users in smart buildings are automatically connected in the Intelligent Communication system. Since many devices and users are interconnected in smart buildings, the probability of cyber-attack is high. Thus, better security is needed in smart buildings and smart environments. It should focus on securing IoT devices, users, and their communication. Hence, this paper developed a deep learning-based anomaly detection framework to dynamically monitor the issues and problems with MUD profiles and detect the anomaly behavior. The Manufacturer Usage Description (MUD) profiles, dynamic user behavior, IoT devices' traffic data the pattern of abnormal/anomaly traffic at the device level is predicted while traffic occurs. The MUD-ML-based model is implemented in Python software, verifying the results.http://www.sciencedirect.com/science/article/pii/S2772671123000815Smart buildingDeep learningManufacturer usage descriptionAnomaly detection |
| spellingShingle | Mirdula S Roopa M MUD enabled deep learning framework for anomaly detection in IoT integrated smart building e-Prime: Advances in Electrical Engineering, Electronics and Energy Smart building Deep learning Manufacturer usage description Anomaly detection |
| title | MUD enabled deep learning framework for anomaly detection in IoT integrated smart building |
| title_full | MUD enabled deep learning framework for anomaly detection in IoT integrated smart building |
| title_fullStr | MUD enabled deep learning framework for anomaly detection in IoT integrated smart building |
| title_full_unstemmed | MUD enabled deep learning framework for anomaly detection in IoT integrated smart building |
| title_short | MUD enabled deep learning framework for anomaly detection in IoT integrated smart building |
| title_sort | mud enabled deep learning framework for anomaly detection in iot integrated smart building |
| topic | Smart building Deep learning Manufacturer usage description Anomaly detection |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123000815 |
| work_keys_str_mv | AT mirdulas mudenableddeeplearningframeworkforanomalydetectioniniotintegratedsmartbuilding AT roopam mudenableddeeplearningframeworkforanomalydetectioniniotintegratedsmartbuilding |