International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 5

• Author(s)Anupam Borthakur, Swapnil Deshmukh
• AffiliationDepartment of Computer Science and Engineering, Assam Engineering College, Guwahati, Assam, India
• Page No.108-112
• Volume, Issue & YearVolume 3, Issue 5, May 2026
• Published On2026/05/15
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350
• DOIhttps://doi.org/10.5281/zenodo.20424053

Abstract

The proliferation of Internet of Things (IoT) devices in industrial automation, smart manufacturing, and critical infrastructure — collectively termed Industrial IoT (IIoT) — has created an expansive and heterogeneous attack surface that traditional signature-based intrusion detection systems (IDS) are ill-equipped to defend. Machine-to-machine communication protocols (MQTT, OPC-UA, Modbus TCP), legacy supervisory control and data acquisition (SCADA) systems, and resource-constrained sensor nodes operating in deterministic real-time environments present unique cybersecurity challenges that demand anomaly-based detection approaches capable of identifying zero-day attacks and novel attack variants from network traffic patterns alone, without prior knowledge of attack signatures. This paper presents a systematic comparative evaluation of three deep learning architectures — a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, a multi-head self-attention Transformer encoder, and a Variational Autoencoder (VAE) — for network intrusion detection on the CIC-IDS-2018 benchmark dataset (16 million traffic flows, 14 attack categories). After class-imbalance correction via Synthetic Minority Oversampling Technique (SMOTE), the CNN-LSTM model achieves macro-average F1-score of 0.9724, outperforming the Transformer (0.9681) and VAE (0.9512) on the held-out test set. Critically, the CNN-LSTM demonstrates superior detection of low-volume attacks (Infiltration: F1 = 0.912) that pose the greatest operational risk in IIoT environments. Inference latency analysis confirms that the CNN-LSTM and Transformer models satisfy real-time detection requirements (latency < 2 ms per flow on NVIDIA RTX 3060) while the VAE's reconstruction-based detection introduces unacceptable latency for real-time deployment. The paper concludes with a deployment architecture recommendation for edge-cloud hierarchical IDS in IIoT environments.

Keywords: Industrial IoT, intrusion detection, deep learning, CNN-LSTM, Transformer, Variational Autoencoder, CIC-IDS-2018, network security, anomaly detection, SMOTE

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