A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network

Adarsh Kumar; Deepak Kumar Sharma

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A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network

机译：A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network

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Outlier detection in a single dimension is not enough to protect the network from known and unknown attacks. There is a strong need to apply multiple steps at multiple stages for combating these attacks. There are various approaches to protect the network from malicious activities and it is an ongoing process to fight against them from a multilayer perspective parallel to networking layering models. This work has considered a multilayered and multi-dimensional approach to protect the hierarchical mobile ad hoc network (MANET) from known and unknown attacks. The proposed multilayered approach consists of ultra-light, light and heavy computational overhead-based outlier detection approaches to combat the attacks. It has been observed that these approaches apply threshold as well as earning processes-based mechanisms for fighting against the attacks. Further, hardware constraint is considered to identify these schemes as ultra-light, light, or heavy. Simulation results show that variations of nodes from 10 to 5,000 vary the cluster formation from 5 to 53 with an error rate of 0.4%.

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来源
《International journal of cloud computing》 |2021年第6期|613-632|共20页
作者
Adarsh Kumar; Deepak Kumar Sharma;
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作者单位

Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India;

Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India;

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正文语种英语
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anomaly detection; active and passive attacks; QoS; performance; clustering; machine learning; threshold measurement; optimisation;

A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network

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