The Address Resolution Protocol (ARP) due to its statelessness and lack of an authentication mechanism for verifying the identity of the sender has a long history of being prone to spoofing attacks. ARP spoofing is sometimes the starting point for more sophisticated LAN attacks like denial of service, man in the middle and session hijacking. In a current voting based method an active technique is used for finding out the legitimate one by collection of voting's from the neighboring host. The drawback of this system is most of the host in a LAN must follow the MR-ARP protocol. In this paper a centralized server will collects all the ip-mac pairs of every host in the LAN and maintains a table of legitimate host. Destination host checks the ip-mac conflict in the LAN and informs about the hacker to the centralized server which takes care of the trusted communication between the participating hosts. Hence, the proposed work detection and prevention of ARP spoofing lead to appreciable result.
<p>Accurate classification of dental caries is crucial for effective oral healthcare. Filters help to increase exposure of the picture taken for the investigation without degrading image quality. Selective median filter is the chosen preprocessing technique that helps to reduce the noise present in the captured image. Dental caries classification system is a model used to detect the presence of cavity in the given input image. Dental caries classification system is evolved with the use of conventional techniques to artificial neural network. Deep learning models are the artificial neural network models that can able to learn the features from the raw images available in the dataset. If this raw image has noise, then it severely affects the accuracy of the deep learning models. In this paper, impact of the preprocessing technique on the classification accuracy is analyzed. Initially, raw images are taken for training on deep learning models without applying any preprocessing technique. This study investigates the impact of Selective median filtering on a dental caries classification system using deep learning models. The motivation behind this research is to enhance the accuracy and reliability of dental caries diagnosis by reducing noise, removing artifacts, and preserving important details in dental radiographs. Experimental results demonstrate that the implementation of Selective median filtering significantly improves the performance of the deep learning model. The hybrid neural network (HNN) classifier achieves an accuracy of 96.15% with Selective median filtering, outperforming the accuracy of 85.07% without preprocessing. The study highlights the theoretical contribution of Selective median filtering in enhancing dental caries classification systems and emphasizes the practical implications for dental clinics, offering improved diagnostic capabilities and better patient outcomes.</p>
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