Kablosuz sensör ağında optimize edilmış özellik seçimi ile hibrit makine öğrenmesi tekniğine dayalı gelişmiş anomali tespit sistemi tasarımı

At present, the Wireless Sensor Network (WSN) plays a pivotal role in the wireless communication system that works based on a large number of sensor nodes. The development of the WSN has become more popular and the nature of versatility resulted in more security concerns and making it hard for the investigation to prevent the anomaly in it. One of the essential and challenging tasks in WSN is the security concern. Detecting the anomaly present in the network becomes the major challenge to ensure the security of WSN. In general, WSNs are affected by a different type of threats that tends the nodes to get damaged and form the wrong determination. Therefore, it is necessary to identify anomalous to minimize the false alarm. Moreover, the quality of the data gathered by the sensor nodes is mainly affected by the anomalies that are produced because of different reasons like reading errors, malicious attacks, failures, and unusual events. Hence it is significant to process the anomaly detection to ensure the quality of the sensor data before it is used to make decisions. In WSN, anomaly detection is the significant process to determine the anomaly or unusual event. However, timely anomaly identification is more complex to function to execute reliably in real-time. For the secure and reliable operation in the WSN, effective anomaly detection is more necessary. However, the standard anomaly detection techniques often fail to adequately secure the privacy of the data and identify the complex, particular, and unique breaches in the WSN. Also, the present anomaly detection models only process under the stationary environment and need to keep all the training data in the node. To address these limitations, a novel Hybrid Machine Learning Technique (HMLT) is introduced to effectively detect the presence of anomalies in WSN. The advanced hybrid technique is designed to enhance the detection performance and safeguard privacy. Initially, the required data from the WSN is collected from the available data resource. Further, the significant feature from the raw data is selected using the optimal feature selection process. Here the Secretary Bird Optimization Algorithm (SBOA) is used to achieve the optimal feature selection. Finally, the HMLT is implemented to perform the detection task, in which the hybrid classifier is the combination of the Deep Belief Network (DBN) along with the Bayesian Learning (BL). The model is specifically developed to identify the occurrence of anomalies in WSN using the HMLT for a given dataset. Extensive comparative analysis is performed to analyze the detection capability of the designed approach along with the conventional model. The resulting outcome defines that the proposed approach performs greater in detecting the anomaly than other standard modelsAt present, the Wireless Sensor Network (WSN) plays a pivotal role in the wireless communication system that works based on a large number of sensor nodes. The development of the WSN has become more popular and the nature of versatility resulted in more security concerns and making it hard for the investigation to prevent the anomaly in it. One of the essential and challenging tasks in WSN is the security concern. Detecting the anomaly present in the network becomes the major challenge to ensure the security of WSN. In general, WSNs are affected by a different type of threats that tends the nodes to get damaged and form the wrong determination. Therefore, it is necessary to identify anomalous to minimize the false alarm. Moreover, the quality of the data gathered by the sensor nodes is mainly affected by the anomalies that are produced because of different reasons like reading errors, malicious attacks, failures, and unusual events. Hence it is significant to process the anomaly detection to ensure the quality of the sensor data before it is used to make decisions. In WSN, anomaly detection is the significant process to determine the anomaly or unusual event. However, timely anomaly identification is more complex to function to execute reliably in real-time. For the secure and reliable operation in the WSN, effective anomaly detection is more necessary. However, the standard anomaly detection techniques often fail to adequately secure the privacy of the data and identify the complex, particular, and unique breaches in the WSN. Also, the present anomaly detection models only process under the stationary environment and need to keep all the training data in the node. To address these limitations, a novel Hybrid Machine Learning Technique (HMLT) is introduced to effectively detect the presence of anomalies in WSN. The advanced hybrid technique is designed to enhance the detection performance and safeguard privacy. Initially, the required data from the WSN is collected from the available data resource. Further, the significant feature from the raw data is selected using the optimal feature selection process. Here the Secretary Bird Optimization Algorithm (SBOA) is used to achieve the optimal feature selection. Finally, the HMLT is implemented to perform the detection task, in which the hybrid classifier is the combination of the Deep Belief Network (DBN) along with the Bayesian Learning (BL). The model is specifically developed to identify the occurrence of anomalies in WSN using the HMLT for a given dataset. Extensive comparative analysis is performed to analyze the detection capability of the designed approach along with the conventional model. The resulting outcome defines that the proposed approach performs greater in detecting the anomaly than other standard models