A Distributed Fog-Based Intrusion Detection Scheme for Industrial Internet of Things using Ensemble Approach

2026-08-19T08:48:41+00:00

The widespread growth of the Internet of Things (IoT) over the past few years has drastically transformed human life. Industrial IoT (IIoT) is a subset of the broader concept of IoT, which is predominantly deployed within the industrial environment. IIoT has revolutionized the industrial environment by automating business processes and enhancing production. However, this tremendous growth of IoT globally has greatly increased the threat of cyberattacks. Thus, there is a need to implement a lightweight, robust security system that can detect such attacks and take preventive measures. In this respect, the design of an intrusion detection scheme (IDS) gains importance in order to detect malicious activities and secure the data from misuse. In this work, a distributed intrusion detection scheme is proposed that involves the use of fog computing, dimensionality reduction, and an ensemble learning technique. The proposed model based on an ensemble learning algorithm is implemented on fog nodes, and the performance of the model has been analyzed using the Edge-IIoTset and Army Cyber Institute (ACI)-IoT2023 datasets. The results demonstrated the efficacy of the proposed model in terms of performance measures such as accuracy, detection rate, precision, and ROC (receiver operating characteristics).

An Intelligent Web-Based Medical Management and Prescription Recommendation System Using Flask, MySQL, and Machine Learning

2026-07-07T04:40:25+00:00

The healthcare sector continues to struggle with coordinating appointment scheduling, prescription management, patient feedback, and emergency-service delivery across disconnected, often manual, workflows. Addressing these issues, this study reports on the formulation, construction, and testing of an intelligent medical management system, a Flask-based web application supported by a MySQL database that brings together the four main users (patient, doctor, druggist, and administrator) in one single, secure, and role-based platform. The system is equipped with a supervised machine-learning component that suggests medications and personalised diet plans based on patient-reported symptoms as well as historical prescription data. Beyond that, the system has modules for appointment scheduling, prescription tracking, emergency-resource management, and centralised feedback handling. The system’s architecture is explained with the help of use-case activity sequence, data-flow, and deployment diagrams, and the system’s implementation is assessed via unit integration system, security, and user-acceptance testing, plus quantitative performance benchmarking and a comparative analysis of machine-learning models for the prescription recommendation task. The experimental evidence shows that the system developed is capable of matching the recommendation accuracy of baseline classifiers, while at the same time it greatly decreases administrative workload against traditional, isolated medical-record systems.

International Journal of Internet of Things and Smart Computing Environment

A Distributed Fog-Based Intrusion Detection Scheme for Industrial Internet of Things using Ensemble Approach

An Intelligent Web-Based Medical Management and Prescription Recommendation System Using Flask, MySQL, and Machine Learning