Cite this DOI

Dr. M. Vanitha, D. M. V. (2023). DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS. *Journal of Science & Technology*, *8*(12), 131–138. https://doi.org/10.46243/jst.2023.v8.i12.pp131-138

@article{drmvanitha2023based,
  author    = {Dr. M. Vanitha, Dr. M. Vanitha},
  title     = {{DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {dec},
  volume    = {8},
  number    = {12},
  pages     = {131--138},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i12.pp131-138},
  url       = {https://doi.org/10.46243/jst.2023.v8.i12.pp131-138},
  language  = {en},
  abstract  = {Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber-attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregationaggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances}
}

TY  - JOUR
TI  - DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS
AU  - Dr. M. Vanitha, Dr. M. Vanitha
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/12/12/
VL  - 8
IS  - 12
SP  - 131
EP  - 138
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber-attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregationaggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances
DO  - 10.46243/jst.2023.v8.i12.pp131-138
UR  - https://doi.org/10.46243/jst.2023.v8.i12.pp131-138
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i12.pp131-138",
    "title": "DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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    "volume": "8",
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    "language": "en",
    "abstract": "Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber-attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregationaggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i12.pp131-138 gives all four in one JSON answer.