Abstract
Abstract: A wide range of industries, including security, healthcare, and industrial settings, currently employ wireless sensor networks (WSNs). With a limited power supply, bandwidth, and energy consumption, WSNs are unique. While traditional networks can be protected in many ways, WSNs cannot be protected in the same way. In order to enhance the service safety of wireless sensor networks, new concepts as well as methodologies were needed. In WSNs, intrusion prevention is the most important issue. For WSN Intrusion Detection (IDS), this research used Dense Artificial Neural Networks (DANN) to develop a DL technique (DeepANN). Compared to the previous models, the proposed Analytical model achieved a 95 percent accuracy rate. The ANN model outperforms the other ML models, with F1 scores of 99, 98, and 96.
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