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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT

Manish Snehi¹, Abhinav Bhandari¹

¹Computer Science and Engineering, Punjabi University, Patiala, Punjab India.

Arabian Journal for Science and Engineering

|January 24, 2022

Summary

A novel Stack-Ensemble framework accurately classifies Internet of Things (IoT) traffic using behavioral attributes, enhancing smart-environment management and security. This advanced IoT device classification achieves 99.94% accuracy.

Keywords:

Classification Deep learning Distributed computing Docker H2O Internet of Things IoT Machine learning Stack ensemble

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Published on: May 15, 2020

Area of Science:

Computer Science
Electrical Engineering
Cybersecurity

Background:

The integration of Industrial 5G, Cyber-Physical Systems, and Industrial Internet of Things (IoT) necessitates efficient management of diverse IoT devices in smart environments.
Accurate characterization and classification of network traffic are crucial for device management, Quality of Service (QoS), and robust security solutions.

Purpose of the Study:

To address the challenges of managing myriad IoT devices and their network traffic in intelligent environments.
To propose a novel intelligent framework for real-time IoT traffic classification leveraging behavioral attributes.

Main Methods:

Development of a novel IoT classification framework utilizing a Stack-Ensemble approach for high-volume, real-time IoT traffic.
Comprehension of flow-level statistical characteristics of IoT devices.
Design of a distributed, scalable, and portable framework architecture with an industry-grade machine-learning pipeline.

Main Results:

The proposed Stack-Ensemble model achieved a high accuracy of 99.94% in classifying IoT traffic.
Comprehensive evaluation of model performance across multiple dimensions, including often-overlooked metrics.
Demonstration of a horizontally scalable architecture that distributes computational load effectively.

Conclusions:

The novel framework provides an effective solution for real-time IoT traffic classification and device management.
The study offers valuable statistical insights into intelligent model performance and experimental results.
The proposed architecture and methodology enhance security solutions against cyber-attacks in smart environments.