Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection

Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection

This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage...

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Bibliographic Details
Main Authors: Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
SCADA
IoT security
malware traffic analysis
critical infrastructures
hybrid deep learning
network intrusion detection
Online Access:https://ieeexplore.ieee.org/document/11028034/
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