Server farms used in web hosting and commercial applications connect multiple servers. Edge computing being a realm of cloud technology is orchestrated with server farms to enhance network efficiency. Edge computing increases the availability of cloud resources and Internet services. The higher availability of services and their ease of access deeply affect the user’s requesting behavior. The anomalous requesting behavior is creating malicious traffic, and enormous amount of such traffics at server farm denies the services to the legitimate users. Categorizing the incoming traffic into malicious and non-malicious traffic at server farm is the foremost criteria to eliminate the attacks, which in turn improves the QoS of the server farm. In the light of preventing the biased usage of the server farm, this paper proposes a SVM classifier based on requesting statistics. The proposed classifier discovers the attacks that deny services to legitimate users in two levels, based on the user’s request behavior. The pattern of arrival, its statistical characteristics and security misbehaviors are investigated at both levels. An incremental learning algorithm is proposed to enhance the learning plasticity of the proposed classifier. The experimental results illustrate that the performance of the proposed two-level classifier with respect to classification accuracy is competently improved with incremental learning.

Web托管和商业应用程序中使用的服务器场连接多个服务器。边缘计算作为云技术的领域与服务器场进行了协调，以提高网络效率。边缘计算可提高云资源和Internet服务的可用性。服务的更高可用性及其易于访问深刻地影响了用户的请求行为。异常的请求行为正在创建恶意流量，并且服务器场中的大量此类流量拒绝了对合法用户的服务。在服务器场中将传入流量分类为恶意和非恶意流量是消除攻击的首要条件，从而提高了服务器场的QoS。考虑到防止对服务器场的偏爱使用，本文提出了一种基于请求统计的支持向量机分类器。提议的分类器基于用户的请求行为，发现了拒绝向合法用户提供服务的攻击的两个级别。在两个级别上都研究了到达模式，其统计特征和安全不良行为。为了提高分类器的学习可塑性，提出了一种增量学习算法。实验结果表明，提出的两级分类器在分类精度方面的性能通过增量学习得到了明显改善。它的统计特征和安全行为均在两个级别上进行了调查。为了提高分类器的学习可塑性，提出了一种增量学习算法。实验结果表明，提出的两级分类器在分类精度方面的性能通过增量学习得到了明显改善。它的统计特征和安全行为均在两个级别上进行了调查。为了提高分类器的学习可塑性，提出了一种增量学习算法。实验结果表明，提出的两级分类器在分类精度方面的性能通过增量学习得到了明显改善。