Detection Methods for Jamming and Spoofing Attacks in IoT Systems

With the increasing deployment of Internet of Things (IoT) and wireless communication systems, the threats posed by jamming and spoofing attacks have become a significant concern. Various detection methodologies have been proposed to mitigate these threats, each with distinct characteristics in terms of complexity, limitations, and efficiency. This study compares five primary detection approaches: signal analysis methodologies, statistical model methodologies, machine learning-based methodologies, blockchain-based methodologies, and hybrid methodologies. Signal analysis methods offer real-time detection but suffer from high computational overhead. Statistical models provide a balance between efficiency and complexity but may struggle with dynamic attack patterns. Machine learning-based techniques demonstrate high adaptability and accuracy; however, they require extensive training data and computational resources. Blockchain-based approaches enhance security through decentralization but introduce latency and scalability challenges. Hybrid methodologies integrate multiple approaches to optimize detection performance while mitigating individual limitations. This comparative analysis provides insights into selecting appropriate detection mechanisms based on specific application requirements and resource constraints.