2022
DOI: 10.1109/access.2022.3163246
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CBiLSTM: A Hybrid Deep Learning Model for Efficient Reputation Assessment of Cloud Services

Abstract: The cloud market is characterized by fierce rivalry among cloud service providers. The availability of various services with identical functionalities on the market complicates the selection decision for service requesters. Although objective trust measurements can be used to evaluate the trustworthiness of services, they are not always available and are static in nature. Subjective approaches are not always viable because they often require repeated service invocations to collect client feedback. To overcome … Show more

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Cited by 13 publications
(7 citation statements)
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References 36 publications
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“…User feedback collected by existing online systems mainly includes user reviews and user ratings. Research based on user reviews includes review feature analysis (Maddy K. et al, 2014;Fontanarava J. et al, 2017;Zhang L. et al, 2022;Jafarian B. et al, 2023) and sentiment analysis (Dang et al, 2020;Nandwani P. et al, 2021;Al Saleh R. et al, 2022;Wankhade M. et al, 2022), etc. However, the number of user ratings collected by many online systems is much larger than the number of text reviews, so our work focuses on an online service reputation measurement method based on user ratings.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…User feedback collected by existing online systems mainly includes user reviews and user ratings. Research based on user reviews includes review feature analysis (Maddy K. et al, 2014;Fontanarava J. et al, 2017;Zhang L. et al, 2022;Jafarian B. et al, 2023) and sentiment analysis (Dang et al, 2020;Nandwani P. et al, 2021;Al Saleh R. et al, 2022;Wankhade M. et al, 2022), etc. However, the number of user ratings collected by many online systems is much larger than the number of text reviews, so our work focuses on an online service reputation measurement method based on user ratings.…”
Section: Related Workmentioning
confidence: 99%
“…Common user feedback mainly includes user comments and user ratings. Recently, research based on user reviews has used the concept of an open world, integrating deep neural networks to obtain more fair and accurate measurements of service reputation (Fontanarava J. et al, 2017;Nandwani P. et al, 2021;Al Saleh R. et al, 2022;Zhang L. et al, 2022;Jafarian B. et al, 2023). However, providing feedback requires a significant investment of time and other costs.…”
Section: Introductionmentioning
confidence: 99%
“…The prevalence of social media platforms and the large number of content and users they host present new challenges in detecting and stopping spam. In their study, Elakkiya et al (2021) introduced a fresh method for identifying spam by utilizing a blend of CNN and BiLSTM models [29], bolstered by a conjoint attention mechanism [30]. The model showcased impressive accuracy across a range of datasets, including those from Twitter and YouTube.…”
Section: Background and Literature Reviewmentioning
confidence: 99%
“…These parts work together to control the flow of information, which lets the network store data for any amount of time and makes LSTMs perfect for tasks that require sequences of different lengths and long gaps between important events [37]. Consequently, LSTMs have become a cornerstone in deep learning (DL) for effectively categorizing and processing sequential and time-series data, overcoming the limitations associated with discharging and disappearing gradients that plagued earlier RNN models [29]. Tashtouth et al [38] and Al-Eidi et al [39] discussed implementing cybersecurity systems and highlighted the dual nature of technological advancements in cybersecurity, offering insights into both the challenges and opportunities presented by novel attack vectors and defensive mechanisms.…”
Section: Nlp Models: Rnn and Lstmmentioning
confidence: 99%
“…BIRCH is used to identify anomalies, and root cause analysis (RCA) is performed by employing a graph that represents anomaly transmission across systems. Additionally, there is recent research in this field as discussed in [44][45][46][47][48][49][50][51][52][53].…”
Section: Literature Reviewmentioning
confidence: 99%