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Widiastuti, Rosalina Yani
Sekolah Tinggi Ilmu Komputer Yos Sudarso
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Edge Computing Enabled Real Time Anomaly Detection Framework for Secure Industrial Cyber Physical Systems Using Lightweight Deep Neural Networks Deny Prasetyo; Suyahman Suyahman; Rosalina Yani Widiastuti; Mursalim Mursalim; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.399

Abstract

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.
Co-Authors Adhi Wibowo Adhi Wibowo Adilla, Axl Agnes Patria W Agus Wantoro Ahmad Jurnaidi Wahidin Anastasia Anastasia Angela Marici Uba Langobelen Antoni Pribadi Antonius Ronny Lie Jaya Brilian Amalsha Katigo Deny Prasetyo Deny Prasetyo Duhita Layla Endang Setyawati Endang Setyawati Haris, Raymond Hermanto , Diwahana Mutiara Candrasari Johan Robert Putra Julius Flandy Soeharjono Lynawati Lynawati Lynawati Lynawati, Lynawati Malindir, Lukas Mikael Billyarta K Mikha El Charisto R Mursalim Mursalim Nasution, Heskia Ngguwa, Angelina Prasuteja, Soni Gunawan Putra, Johan Robert Racma, Dhany Faizal Ria Manurung Ria Manurung, Ria Rosa Ratri Kusuma Hariningsih Sandra, Alenta Bio Setyawan, Antonius Ary Siska Narulita Soeharjono, Julius Flandy Suparno, Agus Suyahman Suyahman Suyahman Suyahman Suyudi Suyudi , Suyudi Suyudi Suyudi Suyudi Suyudi, Suyudi Very Dwi Setiawan Wibowo , Adhi Wicaksono, Wilibrordus Megandika Widjayanti, Carolina Ety Wijaya, Jonathan Briant Yosita Lianawati