Internet of Things (IoT) is a network that provides security for physical objects such as smart home appliance, smart machines and many more. The physical objects are assigned to a unique Internet address known as Internet Protocol (IP) that is used for data communication with the external entities of the network through the internet. The IoT devices are facing security issues due to the rapid increase in attacks that are launched by the intruders during data sharing through the internet. The detection of attacks is essential to provide a strong security mechanism for such threatening attacks. The proposed hybrid optimization algorithm utilizes the combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in this research. The PSO is known for its fast computation speed and has found extensive utility in data training as well as data estimation. The GWO is developed as an intrusion detection approach to classify data and to efficiently detect several of intrusions. The proposed hybrid GWO-PSO uses NSL-KDD data set with binary and multi class problem respectively for showing the effectiveness of the present work. The results obtained better accuracy value of 99.97 % when compared to the existing LSTM-RNN that achieved 97.72% of accuracy, whereas the multi class SVM obtained 98 % and modified rank-based information gain feature selection method showed 99.8 % of accuracy.
INTRODUCTION: Multimedia Broadcast Multicast Service (MBMS) primarily targets broadcasting mobile television contents and video streaming services. Consumers worldwide are rapidly using Dual SIM Dual Standby (DSDS) device that utilizes a single common Radio Frequency Integrated Circuit (RFIC) for supporting more than one Subscriber Identity Module (SIM) cards. These two popular feature requirements are quite contrasting and pose a significant challenge to the design and implementation of User Equipment (UE) to achieve lossless MBMS video performance and Call connectivity Key Performance Indexes (KPIs). OBJECTIVE: This paper targets a cross-layer optimization merging the application-domain quality metrics to the modem level realization of the DSDS scheduler algorithm and enhances the performance. METHOD: The knowledge about the video quality metrics is used to design inputs to the modem scheduler and derive the packet loss thresholds that would be bearable to sustain the desired video quality. RESULTS: Simulation model is prepared based on configurations which are taken from real field values used in commercial network operations. Different configurations of the jitter buffer sizes are defined along with watermarks level to determine the buffer status and corresponding set of actions in terms of DSDS scheduler operation adaptation as a feedback. Based on experimentation, with buffer window of 50ms and 10 % FEC redundancy configuration, optimum performance is determined when RFIC throttling with 25% RFIC rejection for paging occasions is applied. No adverse impact is seen on paging while packet loss performance is optimized. CONCLUSION: Performance of MBMS operation on DSDS device is considered in this paper. Proposed algorithm provides the guidelines for designing the RFIC scheduler for DSDS operation to achieve robust and enhanced MBMS video performance along with maintaining call connectivity KPIs.
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