Article · International Journal of Innovative Research and Scientific Studies · March 6, 2025

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

Салтанат Адилжанова, Murat Kunelbayev, Gulshat A. Amirkhanova, Yesset Zhussupov, Alikhan Tortay

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.

Network Security and Intrusion DetectionInternet of Things and AIIoT and Edge/Fog ComputingIndustrial Internet of Thingsenterprise securityremote monitoring

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

Abstract

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