Abstract

The rise of the Internet of Things (IoT) has already altered contemporary digital ecosystems by enabling the seamless interconnection of heterogeneous devices. Nonetheless, this growth has posed serious security challenges, including a scarcity of resources, device heterogeneity, and exposure to a large attack surface. This work explores the security threats that pose a critical issue for IoT networks, as well as modern detection techniques and prevention methods. To categorize IoT-specific threats, a systematic review of the available literature is conducted, covering network-level, device-level, and application-level threats. The paper also assesses detection techniques, including signature-based, anomaly-based, and machine-learning-based approaches, and their usefulness and limitations in dynamic IoT settings. Moreover, different preventive schemes, such as cryptography, authentication mechanisms, and privacy-preserving communication models, are discussed with respect to their flexibility and scalability. The results indicate that although higher-performing detection models improve threat-detection accuracy, issues of computational cost and real-time responsiveness persist. To ground this synthesis in measured behavior, a controlled benchmarking experiment is additionally conducted, comparing signature-based, anomaly-based, and machine-learning-based detectors on the NSL-KDD intrusion-detection dataset; the experiment quantifies the precision-recall trade-off between these paradigms and reveals that rare attack classes remain largely undetected despite high aggregate accuracy. The paper concludes with a statement on the necessity of lightweight, adaptive, and scalable security frameworks to address evolving IoT threats and ensure robust network protection.

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