AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system Article Swipe

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Mahendra Prasad , Pankaj Pal , Sachin Tripathi , Keshav Dahal ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.21203/rs.3.rs-2190363/v1

An IoT-based monitoring system remotely controls and manages intelligent environments. Deployed sensors communicate themselves and transmit data over wireless communication. A sensor node (insider or outsider) is in the communication range, which can send data to other nodes. Due to such nature, it is more vulnerable to intrusions or attacks. Traditional (threshold based) is not powerful enough to detect intrusions, it shows low detection accuracy. An intrusion detection system is an efficient mechanism to detect malicious traffics and prevents abnormal activities. This paper suggests an intrusion detection framework for the cold storage monitoring system. In this, the temperature affects the environment and harms stored products. A malicious node work as a false data injection attack that manipulates temperature and forwards manipulated data, whereas a flooding attack disturbs the existing network. To handle these attacks, a dataset is generated and collected for training the machine learning techniques. Two machine learning techniques have applied as supervised learning (Bayesian Rough Set) and unsupervised learning (micro-clustering). These intrusion detection methods perform better and show high performance. Moreover, this work also provides the comparative analysis of the generated dataset to other IDS datasets.

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Intrusion detection system Computer science Node (physics) Flooding (psychology) Cluster analysis Wireless sensor network Internet of Things Data mining Artificial intelligence Machine learning Real-time computing Computer network Computer security Engineering Psychology Psychotherapist Structural engineering

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Type: preprint
Language: en
Landing Page: https://doi.org/10.21203/rs.3.rs-2190363/v1
PDF: https://www.researchsquare.com/article/rs-2190363/latest.pdf
OA Status: gold
Cited By: 2
References: 32
Related Works: 10
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DOI: https://doi.org/10.21203/rs.3.rs-2190363/v1

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Title: AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-12-12

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Authors: Mahendra Prasad, Pankaj Pal, Sachin Tripathi, Keshav Dahal

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Landing page: https://doi.org/10.21203/rs.3.rs-2190363/v1

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Concepts: Intrusion detection system, Computer science, Node (physics), Flooding (psychology), Cluster analysis, Wireless sensor network, Internet of Things, Data mining, Artificial intelligence, Machine learning, Real-time computing, Computer network, Computer security, Engineering, Psychology, Psychotherapist, Structural engineering

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Cited by: 2

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