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Security Dynamics in Computer Science Technologies
... More recent examples are [12], [40], and [41]. The uptick of publications in a patent cluster indicates discoveries and technological progress. ...
... Kucharavy and De Guio model energy consumption and infrastructure development as S-curves [46], Andersen [47] shows the prevalence of S-curves in 56 technological groups, ranging from chemicals to non-industrial fields. Adamuthe et al., Priestly et al., and Percia et al. show the prevalence of S-curves in computer-sciencerelated fields [48], [12], [41]. ...
... Moderators then check the classification. We assume the classification to be robust because the taxonomy was reached through consensus by a board of experts, researchers have the incentive to classify their research correctly, and moderators check the classification [41]. We assume each subcategory on arXiv to form an exclusive and exhaustive body of knowledge representing different technologies. ...
Because of the considerable heterogeneity and complexity of the technological landscape, building accurate models to forecast is a challenging endeavor. Due to their high prevalence in many complex systems, S-curves are a popular forecasting approach in previous work. However, their forecasting performance has not been directly compared to other technology forecasting approaches. Additionally, recent developments in time series forecasting that claim to improve forecasting accuracy are yet to be applied to technological development data. This work addresses both research gaps by comparing the forecasting performance of S-curves to a baseline and by developing an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. However, for a minority of emerging technologies, the MAPE increases by two magnitudes. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result. It forecasts established technologies with the same accuracy as the other approaches. However, it is especially strong at forecasting emerging technologies with a mean MAPE 18% lower than the next best result. Our results imply that a simple ARIMA model is preferable over the S-curve for technology forecasting. Practitioners looking for more accurate forecasts should opt for the presented autoencoder approach.
This review paper delves into 5G system security, examining various research papers encompassing various technologies. Through an extensive analysis, explore the multifaceted landscape of 5G security, including but not limited to authentication protocols, encryption mechanisms, threat detection, and mitigation strategies. By synthesizing insights from diverse sources, this paper provides a comprehensive understanding of the current state of 5G security, highlighting both challenges and advancements. The findings presented herein aim to contribute to the ongoing discourse on fortifying the security posture of 5G networks, which is crucial for fostering trust and reliability in the burgeoning era of ultra-fast connectivity.
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