Harnessing Machine Learning for Anomaly Detection and Cybersecurity in IoT Networks

Authors

  • Vipin Saini, Venkata Sri Manoj Bonam, Kalyan Sandhu, Pranadeep Katari, Shashi Thota,

Keywords:

Unsupervised Learning, Hybrid Learning, Threat Mitigation, Network Security, Model Selection, Internet of Things (IoT), Anomaly Detection, Machine Learning, Supervised Learning, Performance Evaluation Metrics.

Abstract

The growth of IoT is unparalleled due to the integration of networked devices in all facets of our lives and enterprises. Innovation thrives on ubiquity, but it also has drawbacks. Numerous IoT gadgets entice nefarious persons who exploit vulnerabilities to create chaos. Unmitigated data breaches, privacy violations, and critical infrastructure failures may transpire. The research investigates machine learning (ML) as an effective safeguard against these dangers.

Machine learning algorithms for anomaly identification in dynamic Internet of Things networks are meticulously chosen. We evaluate the advantages and disadvantages of supervised, unsupervised, and hybrid learning. Supervised learning on labeled datasets of normal and deviant behavior may yield remarkable outcomes. Acquiring sufficient labeled data for IoT scenarios is challenging. IoT networks comprise a greater volume of unlabeled data suitable for unsupervised learning. Nonetheless, their failure to detect anomalies necessitates caution. Integrating several methodologies is stimulating yet necessitates meticulous planning and coordination.

We navigate this labyrinth using various assessment methods. Comprehending the advantages and disadvantages of metrics is essential. Essential metric precision evaluates model effectiveness. The IoT security datasets are inconsistent, rendering accuracy potentially misleading. Accuracy, retention, and the recognition of true positives and abnormalities are crucial. The F1-score equilibrates precision and recall. The computational performance of IoT is essential owing to resource constraints. Evaluating these factors should assist researchers and practitioners in enhancing the security of the IoT ecosystem.

Research improves the resilience of IoT networks. We provide secure and reliable solutions for smart cities, industrial automation, integrated healthcare, and intelligent transportation systems through machine learning and meticulously selected models.

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References

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Published

27.12.2018

How to Cite

Vipin Saini. (2018). Harnessing Machine Learning for Anomaly Detection and Cybersecurity in IoT Networks. International Journal of Intelligent Systems and Applications in Engineering, 6(4), 347–363. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7295

Issue

Vol. 6 No. 4 (2018)

Section

Research Article

License

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