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IoT, Volume 6, Issue 2
June 2025 - 14 articles
Cover Story: Smart grids are increasingly vulnerable to cyberattacks, with the Manufacturing Message Specification (MMS) protocol being a prime target. This study presents a groundbreaking approach that treats network packets as text documents, applying natural language processing techniques combined with deep learning for intrusion detection. Using a bidirectional LSTM autoencoder with TF-IDF vectorization and SVD, the method learns patterns from normal traffic without requiring labeled attack data. The system achieved a 96.63% attack detection rate on MMS traffic, demonstrating that unsupervised AI can effectively protect critical infrastructure. This novel text mining perspective opens new avenues for cybersecurity in industrial control systems. View this paper
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IoT - ISSN 2624-831X