Enhancing quality of experience in mobile edge computing using deep learning based data offloading and cyberattack detection technique
| العنوان: | Enhancing quality of experience in mobile edge computing using deep learning based data offloading and cyberattack detection technique |
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| المؤلفون: | Fahd N. Al-Wesabi, Anwer Mustafa Hilal, Nadhem Nemri, Manal Alohali, Deepak Gupta, Hasan J. Alyamani |
| المصدر: | Cluster Computing. 26:59-70 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Mobile edge computing, Adaptive sampling, Computer Networks and Communications, Computer science, business.industry, Deep learning, Real-time computing, Throughput, Cross entropy, Feedforward neural network, Enhanced Data Rates for GSM Evolution, Quality of experience, Artificial intelligence, business, Software |
| الوصف: | Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client’s proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992. |
| تدمد: | 1573-7543 1386-7857 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::a91f7cd94d489ea60c552272a5d63c39Test https://doi.org/10.1007/s10586-021-03401-5Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi...........a91f7cd94d489ea60c552272a5d63c39 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15737543 13867857 |
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