My Projects

Anomaly Detection in Industrial IoT

The widespread use of edge devices in the Industrial Internet of Things (IIoT) has led to applications in smart manufacturing, intelligent transportation, energy, water, and logistics. While these devices enable real-time decision-making, they face security risks from abnormal IIoT nodes, hindering IIoT development. Edge devices collect time-series data to monitor the behavior of IIoT nodes, which can be used to detect anomalies. However, traditional anomaly detection methods struggle with dynamic complexities and lack labeled data for training. Current unsupervised methods do not fully capture spatial-temporal correlations in multivariate time-series data, leading to limited effectiveness. This paper addresses these challenges using a Federated learning-based AM CNN LSTM unsupervised technique for accurate, real-time anomaly detection in IIoT.

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The PG Level Advanced Certification Course in Deep Learning is offered by the Center of Continuing Education at IISc in collaboration with TalentSprint is a 10-month weekend programme that enables both aspiring and practicing AI/ML professionals to build expertise in Deep Learning. The programme covers the essential theoretical foundations of deep learning and teaches students how to apply them in the real world effectively. The course is best suited for individuals with programming knowledge who want to create a practical understanding of how machine learning algorithms can be developed and optimized for hardware. For more details visit https://iisc.talentsprint.com/deeplearning/index.html