Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Ciar\'an Eising; Lubna Luxmi Dhirani; Mangolika Bhattacharya; Mirza Akhi Khatun

REVIEW

Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.20695 v1 pith:P6VFDHFP submitted 2024-07-30 cs.LG cs.CRcs.CV

Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Mirza Akhi Khatun , Mangolika Bhattacharya , Ciar\'an Eising , Lubna Luxmi Dhirani This is my paper

classification cs.LG cs.CRcs.CV

keywords seriestimeanomaliescnnsdatadetectenvironmentalhealthcare-iot

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92\% accuracy in identifying possible attacks.

Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

discussion (0)