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2013 8th International Conference for Internet Technology and Secured Transactions, ICITST 2013

作者： Sitaram, Dinkar Phalachandra, H.L. Vishwanath, Anush Ramesh, Pramod Prashanth, Meghana Joshi, Akshay G. Desai, Anoop R. Harikrishna Prabhu, C.R. Prafulla, P. Shwetha, R. Yashaswini, A. Center for Cloud Computing and Big Data Department of Computer Science PES Institute of Technology Bangalore India

ISBN: (纸本)9781908320209

Hybrid clouds are increasingly becoming important in cloud computing. We see a rapid raise in the demand for a secure infrastructure that would enable sharing of computing resources between multiple hybrid cloud deployments to facilitate accommodation of situations where the demand outstrips supply, load balancing, and other such infrastructure constraints. From the end user perspective, this would also mean that the end users can host applications with their choice of federated cloud provider, as opposed to choosing from a host of global cloud providers on the market. The following paper describes a federated infrastructure for hybrid clouds, in particular, Openstack. © 2013 Infonomics Society.

关键词： cloud computing federation hybrid clouds security

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Multicloud- auto scale with prediction and delta correction algorithm 2

Multicloud- auto scale with prediction and delta correction ...

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2nd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Harwalkar, Sudheendra Sitaram, DInkar Kidiyoor, Dhanaraj V Milan, L. D'Souza, Ornella Agarwal, Radhika Agarwal, Yamini Center for Cloud Computing and Big Data PES University Dept. of Computer Science Bangalore India

ISBN: (纸本)9781728102832

IT enabled enterprise organizations are embracing multi-cloud to handle their peak loads in addition to their private cloud (own data centers) and the market prediction indicates that by year 2020, about 80% of enterprise will have or working towards multi-cloud enablement for their IT needs. There are multiple products from third-party supporting multi-cloud and also some of the cloud service providers have already enabled the service using proprietary cloud management tools with custom built plug-ins. One of the major challenges in multi-cloud is efficient resource utilization and cost savings without vendor/broker lock-in. At present, multi-cloud interface standardization is still in draft. In this paper we propose handling of cloud burst at peak load with our OpenStack [9] Middleware Framework, which uses default OpenStack services. It also has built-in support for prediction, resource allocation and proactive workload migration techniques which can be integrated easily with cloud provider solutions. © 2019 IEEE.

关键词： Forecasting

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An improved diagonal up-left prediction method for HEVC intra coding 6

An improved diagonal up-left prediction method for HEVC intr...

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2016 6th International Workshop on Computer Science and Engineering, WCSE 2016

作者： Chen, Changnian Shi, Aiwu Su, Nan Chen, E. Cloud Computing and Big Data Research Center College of Mathematics and Computer Wuhan Textile University Wuhan China

ISBN: (纸本)9789811100086

Intra prediction of HEVC supports up to 33 directional modes compared to 8 such modes in H.264/AVC. Employing many more prediction orientations, HEVC can reflect the directional properties of a coding picture more accurately. Although these directional modes are well designed and show good coding performance, the directional prediction strategy can still be improved. In this paper, an alternative diagonal up-left prediction mode is introduced. Two diagonal left reference samples are utilized to predict the mode with appropriate prediction weights. With negligible computing cost, the proposed prediction mode achieves better coding performance than the same directional mode of HEVC.

关键词： Forecasting

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DeepFake Detection Using Deep Learning 1st

DeepFake Detection Using Deep Learning

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Rehman, Mariam Rasool, Mehran Safder, Sadaf Government College University Faisalabad Pakistan National Center in Big Data and Cloud Computing University of Agriculture Faisalabad Pakistan

ISBN: (纸本)9789819979684

A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated images and raised concerns about their misuse and also creating security threats for the society. Deepfake image detection is an essential challenge because the current methods often face inaccuracy and time-consuming issues. The research work employed GAN discriminators to address these issues. The experiment used the CelebA dataset, which contained diverse celebrity images. Moreover, the proposed model consists of components i.e., generator and discriminator. The generator creates fake data from the training images, while the discriminator distinguishes real from the fake images. The result shows that the proposed approach is out-performing with an accuracy of 95.8% in identifying deep fake images, even in the presence of various manipulations. The proposed model is helpful for enhancing trust and security in the society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Generative adversarial networks

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Multi-Stage malicious click detection on large scale web advertising data 1

Multi-Stage malicious click detection on large scale web adv...

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1st International Workshop on big Dynamic Distributed data, BD3 2013 - Co-located with International Conference on Very Large databases, VLDB 2013

作者： Song, Leyi Gong, Xueqing He, Xiaofeng Zhang, Rong Zhou, Aoying Center for Cloud Computing and Big Data East China Normal University 3663 North Zhongshan Road Shanghai China

The healthy development of the Internet largely depends on the online advertisement which provides the financial support to the Internet. Click fraud, however, poses serious threat to the Internet ecosystem. It not only brings harm to the advertisers, but also damages the mutual trust between advertiser and ad agency. Click fraud prediction is a typical big data application in that we normally need to identify the malicious clicks from massive click logs, therefore efficient detection methods in big data framework are much desired to combat this fraudulent behavior. In this paper, we propose a three-stage filtering system to attack click fraud. The serialized filters effectively detect the malicious clicks with decreasing confidence that can satisfy both advertisers and content providers.

关键词： big data

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data integrity checking with reliable data transfer for secure cloud storage

Data integrity checking with reliable data transfer for secu...

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作者： Wang, Yong Tao, Xiaoling Ni, Jianbing Yu, Yong Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guangxi Colleges Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin541004 China

Currently, an increasing number of data owners prefer to store their data on remote servers due to a number of appealing advantages of cloud storage, say convenience and simplicity, scalability of the service and ubiquitous network access. However, outsourced data's transfer becomes a critical requirement for cloud users because of the emergence of various cloud storage services with different qualities of services. Therefore, the users might not only be anxious about the status of their data on cloud servers but also care whether the data are transferred entirely to the new cloud without corruption and whether the data on original cloud are discarded. To address these challenging issues, in this paper, we propose a novel auditing scheme for cloud storage services characterised by secure data transfer, provable data erasure, high error detection probability, confidential data storage. The proposed scheme can guarantee the integrity of remote data when the data are hosted on cloud servers and are transferred between two clouds, and secure deletion of the transferred data on the original cloud. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： data transfer

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Energy Efficiency for HDFS 9

Energy Efficiency for HDFS

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9th IEEE International Conference on cloud computing in Emerging Markets, CCEM 2020

作者： Das, Abhishek Snv, Bhargav Sanketh Reddy, N. Phalachandra, H.L. Pes University Dept. of Computer Science and Engineering Bangalore India Center for Cloud Computing and Big Data Pes University Bangalore India

ISBN: (纸本)9781728195766

The rapidly increasing growth of big data is leading to increased usage of distributed computing and clusters as this can enable tasks to be split and processed in parallel resulting in higher computational efficiency. Environments like Apache Hadoop use Distributed file systems like the HDFS to support large clusters of typically commodity hardware to achieve the same. Commodity hardware present in HDFS consumes significant energy even when not in an active state, and considering our fragile ecosystem, reduction of the carbon footprint and conservation of energy in these environments is the focus of our work. We have evolved on a dynamic approach to Hadoop Distributed File Systems (HDFS) that provides an energy efficient paradigm for Hadoop clusters by segregating the servers in the cluster to two 'Zones', a 'Hot-Zone', and a 'Cold-Zone'. We use energy efficient storage devices (SSDs), to store data that is frequently used for the hot zone. The Cold-Zone would have lesser energy efficient devices (HDDs) and would be used to store data that is not frequently used. The Hot-Zone would always be in an active state to compute while the cold zone would have the HDDs to be in a low power consumption state. We have also enhanced the block placement policy for HDFS to support dynamic block transfer among the two zones. © 2020 IEEE.

关键词： Energy efficiency

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An insider user authentication method based on improved temporal convolutional network

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High-Confidence computing 2023年第4期3卷 87-95页

作者： Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational *** the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal *** of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature *** do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication *** order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for *** results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural *** the final authentication,the Area Under the Curve(AUC)can reach 96%.

关键词： Insider users Mouse dynamics Feature extraction Temporal convolutional network Efficient channel attention mechanism Authentication

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data Catalogs with Artificial Intelligence and Active Metadata: A Case Study of China Mobile 6

Data Catalogs with Artificial Intelligence and Active Metada...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Wang, Jibin Guo, Yanmei Jiang, Yong Shang, Jing Chen, Zhuo Liu, Yu Hu, Qingyuan Yin, Zheng China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9798350388374

In the context of the rapid development of artificial intelligence, telecommunications operators have accumulated an increasing amount of data. China Mobile has developed a set of practical methods of enhanced data catalog in response to current issues such as difficulties in scheduling heterogeneous data from multiple sources, lack of macroscopic views of data usage, unintelligent data operation methods, and high risk of data leakage. By leveraging data virtualization, active metadata management, machine learning, along with other large-scale model technologies, China Mobile has crafted an enhanced data catalog, improving the data management efficiency and business decision-making process, further strengthening its competitiveness in the telecommunication industry. © 2024 IEEE.

关键词： Information leakage

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Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series 19

Discovering All-chain Set with Direction and Graduality Char...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Shaopeng Feng, Chunkai Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Department of Software Engineering Hohhot China

ISBN: (纸本)9798350346558

Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN. © 2022 IEEE.

关键词： Time series

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