Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase ...Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.展开更多
受用户行为和商品属性的影响,线上商品推荐的可靠性难以得到保障,为此,设计基于云计算的线上商品智能推荐系统。将密集计算型ic5云服务器作为系统的硬件装置;在软件设计阶段,利用云计算技术对用户行为进行综合分析,并将其与商品属性进...受用户行为和商品属性的影响,线上商品推荐的可靠性难以得到保障,为此,设计基于云计算的线上商品智能推荐系统。将密集计算型ic5云服务器作为系统的硬件装置;在软件设计阶段,利用云计算技术对用户行为进行综合分析,并将其与商品属性进行匹配分析,确定最终的推荐结果。应用测试结果显示,该系统在不同数据集上的接受者操作特性曲线下面积(Area Under Curve,AUC)表现出了较高的稳定性,且均在0.88以上,表明该系统具有较高的应用价值。展开更多
资源分配策略是云计算研究领域中的一项重要研究点,研究人员提出了多种资源共享与分配策略,然而很少有工作关注不同云计算用户群体的行为习惯对资源分配策略的影响.提出的基于用户行为特征的资源分配策略就是通过统计用户工作习惯与任...资源分配策略是云计算研究领域中的一项重要研究点,研究人员提出了多种资源共享与分配策略,然而很少有工作关注不同云计算用户群体的行为习惯对资源分配策略的影响.提出的基于用户行为特征的资源分配策略就是通过统计用户工作习惯与任务完成时间期望值的变化规律,建立用户行为特征信息表,从而预测出不同时间片内用户的任务提交规律以及用户期望完成时间,动态调整云计算系统的资源分配策略,使得系统在满足用户预期任务完成时间的前提下实现任务并发最大化,提升单位资源的用户满意度.HUTAF(Huawei unitfied test automation framework)云测试平台是华为公司自行研发的云测试平台,并基于该平台开展各种策略下的资源利用率与用户满意度实验.实验结果表明,该策略提升了整个系统在满足用户期望完成时间的前提下的总任务并发数,有效降低了IaaS供应商的运营成本.展开更多
文摘Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.
文摘受用户行为和商品属性的影响,线上商品推荐的可靠性难以得到保障,为此,设计基于云计算的线上商品智能推荐系统。将密集计算型ic5云服务器作为系统的硬件装置;在软件设计阶段,利用云计算技术对用户行为进行综合分析,并将其与商品属性进行匹配分析,确定最终的推荐结果。应用测试结果显示,该系统在不同数据集上的接受者操作特性曲线下面积(Area Under Curve,AUC)表现出了较高的稳定性,且均在0.88以上,表明该系统具有较高的应用价值。
文摘资源分配策略是云计算研究领域中的一项重要研究点,研究人员提出了多种资源共享与分配策略,然而很少有工作关注不同云计算用户群体的行为习惯对资源分配策略的影响.提出的基于用户行为特征的资源分配策略就是通过统计用户工作习惯与任务完成时间期望值的变化规律,建立用户行为特征信息表,从而预测出不同时间片内用户的任务提交规律以及用户期望完成时间,动态调整云计算系统的资源分配策略,使得系统在满足用户预期任务完成时间的前提下实现任务并发最大化,提升单位资源的用户满意度.HUTAF(Huawei unitfied test automation framework)云测试平台是华为公司自行研发的云测试平台,并基于该平台开展各种策略下的资源利用率与用户满意度实验.实验结果表明,该策略提升了整个系统在满足用户期望完成时间的前提下的总任务并发数,有效降低了IaaS供应商的运营成本.
