A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Su, Shen; Sun, Yanbin; Gao, Xiangsong; Qiu, Jing; Tian, Zhihong

doi:10.3390/app9030437

Cited by 41 publications

(28 citation statements)

References 30 publications

(39 reference statements)

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“…The time series behavior of an EV is the basis of traffic generation in the simulation of a large-scale charging scenario. Due to the mathematical simplicity of the approach and the fact that relatively few control parameters are required, the motion synthesis model is easy to be used in simulation platform and can achieve high performance especially in scenarios with large scale of EVs [19].…”

Section: Motion Synthesis Modelmentioning

confidence: 99%

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

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Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure. In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles.

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Section: Motion Synthesis Modelmentioning

confidence: 99%

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Similarly, for the IoT devices, performance improvement, and detection of an anomaly, Shen Su at el. [34] studied the most cited feature selection technique and introduced a feature selection method. For their study, they initially group the IoT sensors as a group for the identification of deployed sensors.…”

Section: Related Workmentioning

confidence: 99%

CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

Shafiq

Tian

Bashir

et al. 2021

IEEE Internet Things J.

Self Cite

373

126

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Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in IoT network. To address the problem, a new framework model is proposed. Firstly, a novel feature selection metric approach named CorrAUC proposed, and then based on CorrAUC, a new feature selection algorithm name Corrauc is develop and design, which is based on wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using AUC metric. Then, we applied integrated TOPSIS and Shannon Entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT dataset and four different ML algorithms. Experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.

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“…When recommending charging piles, we may encounter situations where we recommend the same charging pile to two or more users [26,27]. Under some circumstances, it leads to recommendation conflict if the number of charging piles is insufficient.…”

Section: Dynamic Charging Area Mechanismmentioning

confidence: 99%

A method of chained recommendation for charging piles in internet of vehicles

et al. 2020

Self Cite

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With the popularization of new energy electric vehicles (EVs), the recommendation algorithm is widely used in the relatively new field of charge piles. At the same time, the construction of charging infrastructure is facing increasing demand and more severe challenges. With the ubiquity of Internet of vehicles (IoVs), inter-vehicle communication can share information about the charging experience and traffic condition to help achieving better charging recommendation and higher energy efficiency. The recommendation of charging piles is of great value. However, the existing methods related to such recommendation consider inadequate reference factors and most of them are generalized for all users, rather than personalized for specific populations. In this paper, we propose a recommendation method based on dynamic charging area mechanism, which recommends the appropriate initial charging area according to the user's warning level, and dynamically changes the charging area according to the real-time state of EVs and charging piles. The recommendation method based on a classification chain provides more personalized services for users according to different charging needs and improves the utilization ratio of charging piles. This satisfies users' multilevel charging demands and realizes a more effective charging planning, which is beneficial to overall balance. The chained recommendation method mainly consists of three modules: intention detection, warning levels classification, and chained recommendation. The dynamic charging area mechanism reduces the occurrence of recommendation conflict and provides more personalized service for users according to different charging needs. Simulations and computations validate the correctness and effectiveness of the proposed method.

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A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Cited by 41 publications

References 30 publications

Time series behavior modeling with digital twin for Internet of Vehicles

Time series behavior modeling with digital twin for Internet of Vehicles

CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

A method of chained recommendation for charging piles in internet of vehicles

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