报告详情
Two-Stage System for Anomaly IoMT Detection Gateway
编号：92
访问权限：仅限参会人
更新：2026-11-10 16:44:54 浏览：9次
In-person
报告开始：2026年11月20日 13:45（Asia/Kolkata）
报告时间：15min
所在会场：[S5] Data Science and Intelligent Analytics [S5-1] Data Science and Intelligent Analytics
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摘要
With the ever-increasing number of Internet of Medical Things (IoMT) devices, the need to provide secure and efficient network monitoring has become a ethical concern. Conventional Intrusion Detection Systems (IDS) are usually characterized by either excessive computational requirements or inefficiency in real-time settings. The paper will suggest a hybrid anomaly-based Network Intrusion Detection System (NIDS) that combines threshold-based filtering with an Isolation Forest model with an on-demand machine learning activation mechanism. In this strategy, suspicious traffic, determined by statistical thresholds, is only sent to the machine learning phase, which greatly minimizes unnecessary processing. Early testing on the CICIoMT data revealed that it was accurate but low recall in real-time conditions because of the differences between the fixed datasets and the real-time network traffic. To overcome this shortcoming, a simulated IoMT environment was created with virtual devices to create realistic traffic patterns to facilitate better training and testing. The experimental results indicate that the proposed system enhances the performance of real-time detection and decreases the machine learning workload by about 61.9, which is appropriate to be deployed in the resource-constrained IoMT settings
关键词
Gateway-level security, Hybrid anomaly detection, Threshold-based filtering, Isolation Forest, Machine learning, Energy-efficient computing
报告人
Vinita Bhandiwad
professor Vidyalankar Institute of Technology； Mumbai稿件作者
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重要日期
-
会议日期
11月20日
2026
至11月22日
2026
-
10月31日 2026
初稿截稿日期
-
11月07日 2026
提前注册日期
-
11月22日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Pondicherry University
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICon INDIA 2026
- ic******@usssociety.org
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