Federated Learning for IoT Devices

Gupta, Deena Nath; Kumar, Rajendra; Kumar, Ashwani

doi:10.1007/978-3-030-85559-8_2

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…IoT devices have limited memory and processing power, and they can only process a limited number of predetermined instructions sets [17]. As a result, they are unable to capture, monitor, and analyze communication between IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework

et al. 2022

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The versatility of IoT devices increases the probability of continuous attacks on them. The low processing power and low memory of IoT devices have made it difficult for security analysts to keep records of various attacks performed on these devices during forensic analysis. The forensic analysis estimates how much damage has been done to the devices due to various attacks. In this paper, we have proposed an intelligent forensic analysis mechanism that automatically detects the attack performed on IoT devices using a machine-to-machine (M2M) framework. Further, the M2M framework has been developed using different forensic analysis tools and machine learning to detect the type of attacks. Additionally, the problem of an evidence acquisition (attack on IoT devices) has been resolved by introducing a third-party logging server. Forensic analysis is also performed on logs using forensic server (security onion) to determine the effect and nature of the attacks. The proposed framework incorporates different machine learning (ML) algorithms for the automatic detection of attacks. The performance of these models is measured in terms of accuracy, precision, recall, and F1 score. The results indicate that the decision tree algorithm shows the optimum performance as compared to the other algorithms. Moreover, comprehensive performance analysis and results presented validate the proposed model.

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Section: Introductionmentioning

confidence: 99%