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| Title | Advanced Machine Learning Approach For Designing Intelligent System For Iot Security Framework |
|---|---|
| ID_Doc | 6519 |
| Authors | Mishra D.; Naik B.; Bhoi G. |
| Year | 2024 |
| Published | Studies in Computational Intelligence, 1167 |
| DOI | http://dx.doi.org/10.1007/978-981-97-5204-1_9 |
| Abstract | The significance of security in an IoT network cannot be overstated, as the proliferation of interconnected devices brings forth unprecedented challenges and vulnerabilities, ensuring robust security measures is imperative to safeguard sensitive data and protect against potential cyber threats. In an IoT ecosystem, where devices seamlessly communicate and share data, a breach in security can have far-reaching consequences, compromising personal privacy, business operations, and even public safety. Additionally, as IoT applications extend into critical domains such as health care, smart cities, and industrial automation, the importance of security becomes paramount to prevent disruptions and ensure the integrity and reliability of these interconnected systems. In essence, prioritizing security on an IoT network is crucial for fostering trust, sustaining innovation, and realizing the full potential of the IoT. Machine learning (ML) algorithms empower IoT systems to analyze vast amounts of data generated by interconnected devices, enabling the identification of anomalous patterns and potential security breaches. Therefore, in this study, some advanced ML models such as light gradient boosting machine (LGBM) and Stacked ensemble meta-learner (SEM) are used for analyzing data generated by interconnected devices in IoT networks and detecting anomalous activities and patterns. Further, by leveraging advanced ML for anomaly detection, predictive analysis, and behavior profiling, IoT networks can enhance their resilience and responsiveness to emerging security challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
| Author Keywords | Ensemble learning; IoT security; Light gradient boosting machine; Stacking ensemble learning |
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