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Cloud computing is a global technology for data storage and retrieving. Many organizations are switching their companies to cloud technology, so that they can lease cloud services for use on a membership or pay as you go basis rather than creating their own systems. Cloud service provider and the Cloud service accessibility are the two major problems in cloud computing. The Economic Denial of Sustainability (EDoS) attack is an important attack towards the cloud service providers. The attackers may send continuous requests to the cloud in a particular second. Hence the legitimate user cannot access the data due to heavy cloud traffic. Hence the paid user cannot access the data. However, this problem makes an economical issue to the users. So this paper presented a new technique as, ADS-PAYG (Attack Defense Shell- Pay As You Go) approach using Trust Factor method against the EDoS attack is proposed to improve more number of authenticated users by fixing a threshold value. The algorithm produced an effective result based on response time, accuracy and CPU utilization. The ADS-PAYG solution is applied using MATLAB, which outperforms other Trust factor estimation methods and effectively distinguishes attackers from legitimate users. The detection accuracy is 83.43% for the given dataset and it is high when compared to the existing algorithms.
相似文献This article proposes an improved Newton algorithm as a low complexity signal detection scheme for linear receiver in large scale multiple- input multiple- output (LS-MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system, where a large number of antennas are set up at the base station and active users are with a single antenna system. Data detection for uplink SC-FDMA system is one of the specific challenges due to the significant rise in the dimension of antennas and number of subcarriers. Especially for symbol detection process, LS-MIMO SC-FDMA system with linear detector requires to perform a large matrix inverse computation. Even though linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance, they still introduce high computational complexity and obliviously involve in the computation of matrix inversion. Therefore, a design of complexity reduction algorithm based near-optimal detector for LS-MIMO SC-FDMA system attains research interest. The improved Newton algorithm is employed to obtain linear detection solution which iteratively performs matrix free-inversion operation. The new algorithm performs matrix–matrix multiplication into matrix–vector multiplication, which substantially reduces receiver detection complexity. The efficacy of the proposed method is investigated at 16-QAM. Both ZF and MMSE criteria are proposed and compared through simulations. Simulation results illustrate that the proposed scheme outperforms the conventional detection schemes and exhibits near-optimal performance with a small number of iterations. Further, bit-error-rate performance is closer to classical linear detector with affordable computational complexity.
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