Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.
Education is one of the areas with a higher impact on digitalization, which takes various forms such as education through digital devices and technology to improve the learning process. Online and tangible computing platforms have become more interested in pursuing active teaching through educational technologies within the curriculum. Some common problems and considerations can be addressed, such as access, capacity, financing, periodic progress measurements, and evaluation results. The productive learning activity can be immediately and constantly monitored by proposing Digital Tangible Intelligent Monitoring systems (DTIMS). The decision tree method’s teaching methodology can efficiently perform this proposed system, which monitors the former challenges. At the same time, the next is resolved by a dynamic evaluation process based on the Internet of Things (IoT). The research is evaluated using the education systems currently adopted. The results highlighted the potential of the proposed model and helped to gain information in digital teaching. The simulation analysis is performed based on accuracy 97.69%, vulnerability 91.09%, and efficiency, proving the proposed framework’s reliability of 85.10%.
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