Recurrent Integrated CNN Gate (RICG): A Dynamic Deep Learning Model for Security and Efficiency Enhancement in Cyber-Physical Systems

G. Ramesh (), Geeta C. Mara, S. Sridhar (), D. N. Pughazendi and N. Thillaiarasu ()
Additional contact information
G. Ramesh: NMAM Institute of Technology, NITTE (Deemed to be University), Department of ISE
Geeta C. Mara: REVA University, School of Computing and Information Technology
S. Sridhar: School of Advanced Studies, S-VYASA University, City campus
D. N. Pughazendi: SRM Institute of Science and Technology (Ramapuram Campus), Faculty of Science and Humanities
N. Thillaiarasu: REVA University, School of Computing and Information Technology

A chapter in Reliability in Cyber-Physical Systems: The Human Factor Perspective, 2026, pp 1-29 from Springer

Abstract: Abstract Cyber-Physical Systems (CPSs) are the backbone of essential services such as critical infrastructural services, smart cities, and industrial automation. It throws concerns around real-time decision making, security breaches, and resource optimization that it is now facing more and more. The paper introduces a new proposition, a Recurrent Integrated Convolutional Neural Network Gate (RICG) model: a dynamic architecture, deep-learning based, useful in increasing security and efficiency in a CPS environment. The RICG model brings together the spatio-feature extraction facility of Convolutional Neural Network (CNN) with the temporal learning power of Recurrent Neural Networks (RNN) to function as an adaptive decision-making mechanism. An integrated gating structure dynamically regulates the way extracted spatial-temporal features flow within the model, hence prioritizing the security-critical data and filtering potential anomalies. Unlike other traditional models, RICG employs context-aware recurrent gates, which adjust information persistence intelligently, making it significantly effective for detecting cyber threats such as intrusion attempts, unauthorized access, and data tampering in real-time. The model also boosts the performance exhibited for the system as it predicts workload patterns, thus balancing resource allocation and significantly reducing latency. Extensive simulations on benchmark CPS datasets showed that the RICG model is by far the best among all typically deep learning methods for providing much better accuracy in less time and equally tests more in terms of robustness in detecting anomalies under even quite dynamic network conditions. This research, thus, opens a vast and smart horizon for the future of intelligent industrial systems and critical infrastructure protection as it nurtures a server framework of secure CPS while it continues to keep the very high efficiency promises. Thus, it heralds the proposed RICG model as a next step towards CPS, autonomously, self-protected on-the-fly reliable decision making even in the most complex ever-evolving threat landscapes.

Keywords: Cyber-physical systems; CNN gate; Deep learning; Anomaly detection; Decision-making and security enhancement (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-032-09917-4_1

Ordering information: This item can be ordered from
http://www.springer.com/9783032099174

DOI: 10.1007/978-3-032-09917-4_1

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().