A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network

2025-09-24 · Bilal Dalgic, Betul Sen, Muge Erel-Ozcevik arxiv

Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framework for short-term anomaly detection that uses an SDN-based DT. Using a comprehensive dataset, time-aware labeling of features, and a comprehensive evaluation of various machine learning models, we propose a novel SD-TWIN-based anomaly detection algorithm. According to the performance of a new real-time SD-TWIN deployment, the GPU- accelerated LightGBM model is particularly effective, achieving a balance of high recall and strong classification performance.

📄 PDF Abstract BibTeX arXiv:2509.20068

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly Detection

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

2026-01-06 · Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker 외 arxiv

To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dime…

Anomaly Detection

Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence

2021-10-07 · Jana Kemnitz, Thomas Bierweiler, Herbert Grieb, Stefan von Dosky 외

The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification mode…

Anomaly Classification Classification

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

2021-01-30 · V. Priya, I. Sumaiya Thaseen, Thippa Reddy Gadekallu, Mohamed K. Aboudaif 외

Generally, the risks associated with malicious threats are increasing for the IIoT and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based …

Anomaly Detection Intrusion Detection speech-recognition Speech Recognition

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

2020-07-19 · Yi Liu, Sahil Garg, Jiangtian Nie, Yang Zhang 외

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, dat…

Anomaly Detection Federated Learning Time Series Time Series Analysis

Minimal-Configuration Anomaly Detection for IIoT Sensors

2021-10-08 · Clemens Heistracher, Anahid Jalali, Axel Suendermann, Sebastian Meixner 외

The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equ…

Anomaly Detection Feature Engineering