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In this digital world, clouds are one of the valuable resources which are widely available everywhere. In fulfilling services, it plays a vital role. Cloud is an optimum service provider in all areas. However, to satisfy the emergency requirement of resource allocation, clouds face many execution and design issues. Increasing demands of services in all areas affects the emergency service requirements due to bandwidth bottleneck, network performance problem, network size, and communication. The main issue is reducing latency produced by the processing time for queues by machines and other network intermediate processes. In the process of delay minimization, the initial stage is admission of the request, which will be scheduled and lined up it in an emergency queue. This process reduces the key time of handling requests. To fulfill this emergency resource requirement, a resource scheduling and queuing method is proposed. The algorithm is organized in two stages, where the priorities of a service will be considered for queuing and scheduling. The resource or service allocation will be carried out in a faster way with the combination of the gray wolf and the multidimensional queuing algorithms.

Problem The Internet of Things (IoT) makes developers for integrating various collection of device, saving, and processing a huge amount of data, allowing new services for improving several personal and professional activities. But, the privacy problems occur with a large amount of data produced and therefore the solutions on the basis of blockchain with sidechain technology have been developed to overcome these problems. Aim Blockchain powered IoT networks have high security levels, and are resilient to most network attacks. In order to deploy blockchains, a large set of hashing, encryption, and linking algorithms are implemented in the network. One of the recent developments is the sidechains and these sidechains reduce computational complexity during block creation, and thereby reduce the effect of high delay and high‐power consumption in the IoT network. But maintaining these sidechains requires an additional computational component, which adds to the computational complexity of the underlying system. To reduce the complexity of this maintenance while keeping the inherent advantages of side chaining networks, this article proposes a sidechain creation, updation, merging, and scanning processes. Methods In this article, the sidechain creation is done by modified two‐way peg protocol and updated by hybrid delegated practical byzantine fault tolerance‐delegated proof of stake (DPBFT‐DPOS), merging via continuous network information analysis and scanning via graph‐based searching mechanism to improve the Quality of Service (QoS) network and security. Results The proposed methodology achieves an accuracy and F1‐score of about 98.6% and 99.5%, reduces end‐to‐end communication delay by 10% while increasing the energy efficiency by 15%, and improving the throughput by 15% when compared to other existing methods. Conclusion The IoT provides the possibility of creating real time data and the combination of the IoT with blockchain moves beyond authorization and financial recording. Then the utilization of sidechain provide better communication among devices and better sophistication of the data processing.

Internet of Things (IoT) botnet attacks are considered an important risk to information security. This work mainly focusing on botnet attack detection targeting various IoT devices. In this work, feature generation and classification are the two major processes considered for attack detection. Generative adversarial network (GAN) is applied for the feature generation process. GAN has generator and discriminator. Here effective generator network is introduced by applying added convolution layers with batch normalization and rectified linear unit activation function. In this proposed system, a novel network called the data perception network is proposed with scale fused architecture. The data perception network is developed to determine generator's efficiency in generating fake data similar to original data. This perception network is also considered for estimating loss function by analyzing in different scales. Hence, the major strength of this network is that highly reliable data are provided using the synthesized data. An efficient network architecture called scale fused bidirectional long short term memory attention model (SFBAM) is applied for the classification process. The proposed model is evaluated using the IoT-23 dataset, which can differentiate between benign and malicious data in IoT attacks. Compared to existing models, this proposed model provides effective results by improving accuracy and reducing loss.

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The Software Define Network (SDN) integrated with Internet of Things (IoT) reduces the scalability of IoT devices by managing the network, however the SDN are easily vulnerable to attacks as they used centralized controller for managing the network which can be easily manipulate by the attackers. The existing approaches focused on secure access control to the SDN controller but limits with controller scalability and trust management. By leveraging the problems in existing works, we propose SDMAC-Secure DynaMic Access Control framework which improves the security and provide efficient services to entities. Initially, all the users and applications are registered with attributes based on the registration, the authentication is performed to ensure the legitimacy. The policies are generated for the legitimate users by using Soft Actor Critic (SAC) which considers attributes, actions permitted, and temporal features to enhance network security, the conflicts between the policies are reduced by validating and storing the policies to database by the administrator. The proposed work is validated using iFog Sim tool and the performance comparisons between proposed and existing works are validated with several metrics. The simulation result shows that the proposed model work outperforms better than existing works.

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