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| Title | A Mathematical Model For Enhancing Cybersecurity In Iot Networks Using Lstm-Based Anomaly Detection And Optimization |
|---|---|
| ID_Doc | 2510 |
| Authors | Florrence J.M.; Antoinette A.; Buvaneswari S.; Wanare A.L.; Vashistha A.; Mulpuri M.; Rambabu R. |
| Year | 2025 |
| Published | Communications on Applied Nonlinear Analysis, 32, 2 |
| DOI | http://dx.doi.org/10.52783/cana.v32.1706 |
| Abstract | The widespread adoption of Internet-of-Things (IoT) devices has heralded an explosion in potential attack surfaces with varying capabilities and a wide variety of vulnerabilities. Because of this, IoT networks have become a favorable choice for many cyber-attacks due to the difficulty and complexity that traditional cybersecurity approaches face in managing these types of networks with large numbers up to millions of users leading these attacks to pose significant risks from anomalous behavior. Current approaches such as rule-based intrusion detection system (IDS) and signature-based models are inadequate to be utilised in the dynamic IoT enclaves where they tend to generate too many false positives and may miss unknown threats. In this study, motivated by hybrid methods utilizing machine learningbased anomaly detection wrapped around optimization algorithms, we suggest a full-fledged mathematical model applying these possible solutions for cybersecurity in IoT networks. This model uses a variety of unsupervised learning techniques in order to detect and remediate new threats dynamically at run time. The models learn the optimal thresholds for detection and resource allocation to perform the fastest possible response under low resources constraints, by leveraging optimization algorithms like genetic algorithm or particle swarm optimization. It shows a significant enhancement in anomaly detection accuracy and reduction of false positives compared to conventional methods. Nevertheless, problems in measurement overhead, model scalability and management of big IoT environments characterized by low-computation resources are still being faced. Nevertheless, the proposed model is promising for large-scale applications (e.g., smart cities, industrial IoT, and healthcare applications) in a critical context where cyber threats should be detected on time to help guarantees integrity of operation. Our results indicate that a machine learning-based intrusion detection system in conjunction with optimization techniques can be developed into solid and adaptable cybersecurity infrastructure to safeguard the expanding IoT world. © 2025, International Publications. All rights reserved. |
| Author Keywords | adoption; algorithm; allocation; anomaly; detection; intrusion; model; networks; optimization; scalability; techniques |
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