AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system Article Swipe
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· 2022
· Open Access
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· 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.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2190363/v1
- https://www.researchsquare.com/article/rs-2190363/latest.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311123179