Denial of service (DoS) attack is the menace to private cloud computing environment that denies services provided by cloud servers leading to huge business losses. Efficient DoS attack detection mechanisms are demanded which necessitates the extraction of features for its best performance. The lacuna in the existing feature extraction based detection systems is the sensitiveness of initial cluster center which leads to high false alarm rate and low accuracy. In this paper, this issue is addressed by proposing a class center based triangle area vector (CCTAV) method which computes the mean of target classes individually and extracts the correlation between features. Mahalanobis distance measure is used for profile construction and DoS attacks detection. The proposed CCTAV method is tested with five publicly available datasets and compared with existing methods. It is noticed that the proposed statistical method reduces the complexity of feature extraction and enhances the attack detection process. The proposed approach is evaluated by conducting tenfold cross validation to compute 95% confidence interval. It is evident that the accuracy obtained for all the datasets are within the confidence interval. Further, the proposed CCTAV method provides significant results compared to the state-of-the-art attack detection methods.

拒绝服务（DoS）攻击是对私有云计算环境的威胁，该环境会拒绝由云服务器提供的服务，从而导致巨大的业务损失。需要有效的DoS攻击检测机制，这需要提取功能以实现最佳性能。现有的基于特征提取的检测系统的缺陷是初始聚类中心的敏感性，这导致了较高的虚警率和较低的准确性。在本文中，通过提出一种基于类中心的三角面积向量（CCTAV）方法来解决此问题，该方法可单独计算目标类的平均值并提取特征之间的相关性。Mahalanobis距离度量用于配置文件构建和DoS攻击检测。建议的CCTAV方法已通过五个公开可用的数据集进行了测试，并与现有方法进行了比较。值得注意的是，提出的统计方法降低了特征提取的复杂度，并增强了攻击检测过程。通过进行十倍交叉验证来计算95％的置信区间，对提出的方法进行了评估。显然，所有数据集的准确性都在置信区间内。此外，与最新的攻击检测方法相比，提出的CCTAV方法可提供重要的结果。显然，所有数据集的准确性都在置信区间内。此外，与最新的攻击检测方法相比，提出的CCTAV方法可提供重要的结果。显然，所有数据集的准确性都在置信区间内。此外，与最新的攻击检测方法相比，提出的CCTAV方法可提供重要的结果。