Aim:The aim of this study was to evaluate retention & fracture resistance of different fibre posts.Methodology:90 extracted human permanent maxillary central incisors were used in this study. For retention evaluation, after obturation, post space preparation was done in all root canals and posts were cemented under three groups. Later, the posts were grasped & pulled out from the roots with the help of a three-jaw chuck at a cross-head speed of 5mm/min. Force required to dislodge each post was recorded in Newtons. To evaluate the fracture behavior of posts, artificial root canals were drilled into aluminium blocks and posts were cemented. Load required to fracture each post was recorded in Newtons.Results:The results of the present study show the mean retention values for Fibrekleer Parallel post were significantly greater than those for Synca Double tapered post & Bioloren Tapered post. The mean retention values of the Double tapered post & the tapered post were not statistically different. The Synca Double tapered post had the highest mean load to fracture, and this value was significantly higher than those of FibreKleer Parallel & Bioloren Tapered post. The mean fracture resistance values of Parallel & tapered post were not statistically differentConclusions:This study showed parallel posts to have better retention than tapered and double tapered posts. Regarding the fracture resistance, double tapered posts were found to be better than parallel and tapered posts.
The advancements of technology are playing a significant role in protecting the data from intruders. In this paper, a robust network intrusion detection system (IDS) is proposed for Internet of Things (IoT) using deep learning approaches. The type of intrusions we adopted in this work are distributed denial of service (DDoS) and replay attack. Our proposed work is divided into three sections, namely, node deployment, threat detection modelling, and prevention modelling. For detection, ensemble algorithm has been used, i.e., deep neural network (DNN) and support vector machine (SVM). SVM is used to identify the suspected route and DNN is used to identify the suspected node out of suspected routes. The chosen route ensures that it is prevented from attackers by incorporating the throughput and packet delivery ratio (PDR). The simulation results are obtained on the basis of accuracy, recall, precision, and F-measure to determine the effectiveness of the proposed approach. The precision, recall, F- measure, and accuracy of correctly identified intruders are 98.12%, 98.04%, 94.88%, and 98.68%, respectively, which is an improvement over the previous studies. The efficacy of the designed model for IoT is compared with the existing approaches.
The latest buzzword in internet technology nowadays is the Internet of Things. The Internet of Things (IoT) is an ever-growing network which will transform real-world objects into smart or intelligent virtual objects. IoT is a heterogeneous network in which devices with different protocols can connect with each other in order to exchange information. These days, human life depends upon the smart things and their activities. Therefore, implementing protected communications in the IoT network is a challenge. Since the IoT network is secured with authentication and encryption, but not secured against cyber-attacks, an Intrusion Detection System is needed. This research article focuses on IoT introduction, architecture, technologies, attacks and IDS. The main objective of this article is to provide a general idea of the Internet of Things, various intrusion detection techniques, and security attacks associated with IoT.
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