Development of a data collection and storage system for remote monitoring and detection of security threats in the enterprise

Saltanat Adilzhanova (), Murat Kunelbayev (), Gulshat Amirkhanova (), Yesset Zhussupov () and Alikhan Tortay ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 176-196

Abstract: As the Industrial Internet of Things (IIoT) expands, maintaining a high level of security and reliability is becoming increasingly important for uninterrupted operations. Encryption (TLS/SSL and AES-256) and intrusion detection and prevention systems (IDPS) are essential. In addition, the platform uses neural network algorithms, namely long short-term memory (LSTM) and hybrid CNN-LSTM models, to identify anomalies in real time, which contributes to a rapid response to potential failures or cyber threats. Through the use of model compression and explainable AI (XAI) techniques, the architecture adapts to a variety of industrial scenarios without compromising performance or transparency, helping industry professionals strengthen security measures and improve real-time anomaly detection in the ever-evolving IIoT landscape.

Keywords: Anomaly detection; Data encryption; Data storage; Industrial internet of things; Neural networks; Remote monitoring; Security architecture. (search for similar items in EconPapers)
Date: 2025
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