An Graph-Based Adaptive Method for Fast Detection of Transformed Data Leakage in IOT Via WSN

Yu, Xiang; Qiu, Jing; Yang, Xianfei; Cong, Yue; Du, Lei

doi:10.1109/access.2019.2942335

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…From the experimental setup, the performance of the proposed MACS is assessed using the metrics aggregation loss, time delay, false rate, throughput, and verification time. For a comparative analysis, the existing methods, such as CSDA, 24 LSDAR, 18 and AFGU, 16 are considered.…”

Section: Discussionmentioning

confidence: 99%

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Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Gunasekaran

2023

Int J Communication

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SummaryA wireless sensor network (WSN) is a network of autonomous, small sensors that can detect, collect, and send data about their surrounding environment. In the Internet of Things (IoT) infrastructure, WSNs are the smart devices that provide the platform with resource input. Security breaches and insider attacks are possible due to the WSN's resource‐constrained design. However, the IoT platform's intelligence may be extended to WSN nodes for managing device and data‐level security. This paper proposes Monitored Access Constraint Security (MACS) to ensure the privacy of data collected via the ubiquitous processing enabled by the Internet of Things. The IoT platform performs frequent checks on the quality of the interactions between the various nodes to ensure that they are functioning properly and that the sensor aggregation instances are accountable. Node liability is considered while adjusting the aggregate level and the continuity. The method guarantees secure information from the environment and the data sources. The quality of the data gathered in the suggested technique is evaluated based on node liability and information extraction feature. Accordingly, security measures are implemented at data gathering and filtering levels and then assessed using a recurrent learning process. Since there are fewer security breaches overall, the rate of aggregation increases. Aggregation loss, delay time, false rate, throughput, and verification time are used to evaluate the performance.

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Section: Discussionmentioning

confidence: 99%

“…It is processed based on the encoding and decoding method, which addresses eavesdropping. Yu et al 16 have proposed an Adaptive Feature Graph Update model (AFGU), which addresses data leakage prevention (DLP). Here, the initial step is based on processing the confidential data, and then, the update of features is acquired.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Gunasekaran

2023

Int J Communication

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“…The energy is uniformly distributed between the CH and the members by the thresholds, thus the CH based thresholds are implemented by the Stable Election Protocol (SEP) [9]. The major issues in IoT is the leakage of data, which is overcome with the assistance of the Adaptive Feature Graph Update model (AFGU) [10]. In IoT, the sensor node is battery dependent and to increase the life of the battery, the Real-time Data Collection Model (RDCM) [11] is utilized.…”

Section: Introductionmentioning

confidence: 99%

Smart-Grid Monitoring using IoT with Modified Lagranges Key Based Data Transmission

Morarji¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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One of the recent advancements in the electrical power systems is the smart-grid technology. For the effective functioning of the smart grid, the process like monitoring and controlling have to be given importance. In this paper, the Wireless Sensor Network (WSN) is utilized for tracking the power in smart grid applications. The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data. The data is transmitted quickly by using the Probabilistic Neural Network (PNN), which aids in identifying the shortest path of the nodes. While transmitting the data from the smart grid to the (Internet of Things) IoT web page, it is secured by introducing the secret keys between the neighbouring nodes through the process of key-management. In this method, the combination of Lagrange's theorem and the Location Based Key (LBK) management is used for better security performance. This approach delivers optimal performance in terms of security, throughput, packet loss and delay, which are comparatively better than the existing methods.

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“…Currently, deep learning methods, such as Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), have been used by many scholars to realize regression prediction ( Sezer, Berat & Ahmet, 2020 ; Shafiq et al, 2020b ; Tian et al, 2020 ; Yu et al, 2019b ; Yu et al, 2019a ). LSTM is a special recurrent neural network that transmits information of network state through gating mechanism to realize network memory function, which can alleviate not only the problem of long-range dependencies, but also the gradient disappearance and gradient explosion ( Hou et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Background The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry. Methods A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What’s more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established. Results The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.

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An Graph-Based Adaptive Method for Fast Detection of Transformed Data Leakage in IOT Via WSN

Cited by 8 publications

References 35 publications

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Smart-Grid Monitoring using IoT with Modified Lagranges Key Based Data Transmission

Strip thickness prediction method based on improved border collie optimizing LSTM

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