首页> 外文期刊>Journal of computational and theoretical nanoscience >UBP-Trust: User Behavioral Pattern Based Secure Trust Model for Mitigating Denial of Service Attacks in Software as a Service (SaaS) Cloud Environment
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UBP-Trust: User Behavioral Pattern Based Secure Trust Model for Mitigating Denial of Service Attacks in Software as a Service (SaaS) Cloud Environment

机译:UBP-Trust:基于用户行为模式的安全信任模型,用于减轻软件拒绝服务拒绝服务(SAAS)云环境

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摘要

The problem of security enforcement in cloud environment has been discussed in number of situations and the most approaches uses minimum number of features to mitigate the denial of service attacks in cloud environment. The methods suffers with the problem of poor detection accuracyand false classification ratio, to overcome the issue, we propose a novel approach to mitigate the denial of service attacks in SaaS layer of cloud environment. This paper discusses a UBP-Trust model, which monitors the behavioral patterns of the users of cloud environment at different situations.Based on the monitored results, the method generates user behavior pattern which represents, the number of times the user has accessed the service, the number of times the service has been accessed and finished successfully, the amount of data being sent, the number of false invocation, thevariance of protocol and so on. Using all these features considered the method generates the behavioral pattern and used to compute the user trust weight for each user being monitored. Based on the weight computed, he will be decided as malicious or genuine and based on which the method restrictthe user from accessing the service. The proposed method produces efficient results in DDOS detection accuracy and produces less time complexity and false classification ratio.
机译:在云环境中讨论了云环境中的安全实施问题,并且最多方法使用最小的功能来减轻云环境中的拒绝服务攻击。这些方法遭受了检测精度差的问题,克服了这个问题,我们提出了一种新颖的方法来减轻云环境萨瓦斯层的拒绝服务攻击。本文讨论了一个UBP-Trust模型,它监视云环境的用户的行为模式,在不同的情况下。基于监视结果,该方法生成表示的用户行为模式,表示用户访问服务的次数,已成功访问并完成服务的次数,发送数据量,错误调用的数量,协议的The Active等。使用所考虑的所有这些功能,该方法生成行为模式,并用于计算正在监视的每个用户的用户信任权重。基于计算的重量,他将被确定为恶意或真实,并基于该方法限制用户访问服务。该方法在DDOS检测精度中产生有效的结果,并产生较少的时间复杂性和假分类比率。

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