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Cloud computing
An \"overview of the implications of the cloud phenomenon and the opportunities and risks associated with it\"-- Provided by publisher.
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A survey on security challenges in cloud computing: issues, threats, and solutions
Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.
Journal Article
Terahertz topological photonics for on-chip communication
The realization of integrated, low-cost and efficient solutions for high-speed, on-chip communication requires terahertz-frequency waveguides and has great potential for information and communication technologies, including sixth-generation (6G) wireless communication, terahertz integrated circuits, and interconnects for intrachip and interchip communication. However, conventional approaches to terahertz waveguiding suffer from sensitivity to defects and sharp bends. Here, building on the topological phase of light, we experimentally demonstrate robust terahertz topological valley transport through several sharp bends on the all-silicon chip. The valley kink states are excellent information carriers owing to their robustness, single-mode propagation and linear dispersion. By leveraging such states, we demonstrate error-free communication through a highly twisted domain wall at an unprecedented data transfer rate (exceeding ten gigabits per second) that enables real-time transmission of uncompressed 4K high-definition video (that is, with a horizontal display resolution of approximately 4,000 pixels). Terahertz communication with topological devices opens a route towards terabit-per-second datalinks that could enable artificial intelligence and cloud-based technologies, including autonomous driving, healthcare, precision manufacturing and holographic communication.Robust terahertz wave transport is demonstrated on a silicon chip using the valley Hall topological phase. Error-free communication is achieved at a data rate of 11 Gbit s−1, enabling real-time transmission of uncompressed 4K high-definition video.
Journal Article
Parallel convolutional processing using an integrated photonic tensor core
With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)
1
, the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important
2
. Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (10
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MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs
3
). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates
3
–
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, ultralow-loss silicon nitride waveguides
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,
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, and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal–oxide–semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.
An integrated photonic processor, based on phase-change-material memory arrays and chip-based optical frequency combs, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second, is demonstrated.
Journal Article
Genomics: data sharing needs an international code of conduct
Efforts to protect people’s privacy in a massive international cancer project offer lessons for data sharing.
Efforts to protect people’s privacy in a massive international cancer project offer lessons for data sharing.
Coloured scanning electron micrograph of a migrating breast cancer cell
Journal Article
Intrusion detection in cloud computing based on time series anomalies utilizing machine learning
The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.
Journal Article