In spite of the tangible advantages of cloud computing, it is still vulnerable to potential attacks and threats. In light of this, security has turned into one of the main concerns in the adoption of cloud computing. Therefore, an anomaly detection method plays an important role in providing a high protection level for network security. One of the challenges in anomaly detection, which has not been seriously considered in the literature, is applying the dynamic nature of cloud traffic in its prediction while maintaining an acceptable level of accuracy besides reducing the computational cost. On the other hand, to overcome the issue of additional training time, introducing a high-speed algorithm is essential. In this paper, a network traffic anomaly detection model grounded in Catastrophe Theory is proposed. This theory is effective in depicting sudden change processes of the network due to the dynamic nature of the cloud. Exponential Moving Average (EMA) is applied for the state variable in sliding window to better show the dynamicity of cloud network traffic. Entropy is used as one of the control variables in catastrophe theory to analyze the distribution of traffic features. Our work is compared with Wei Xiong et al.’s Catastrophe Theory and achieved a maximum improvement in the percentage of Detection Rate in week 4 Wednesday (7.83%) and a 0.31% reduction in False Positive Rate in week 5 Monday. Additional accuracy parameters are checked and the impact of sliding window size in sensitivity and specificity is considered.

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This paper presents a model for resolving two main issues of time in e‐commerce. The first issue is the time value of e‐commerce that represents the value of each moment of the commerce time from the perspective of buyers and sellers. Buyers and sellers can use this model to calculate the time value at each moment of time and accordingly decide whether it is profitable to buy or sell at that moment. The second issue is to allow buyers or sellers to increase their savings or decrease their costs by changing each of the factors governing the time value model of the concerned e‐commerce. We present relevant model specifically for Amazon e‐commerce to present a proof of concept of our proposed models.

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Discovery of useful information and valuable knowledge from transactions has attracted many researchers due to increasing use of very large databases and data warehouses. Furthermore most of proposed methods are designed to work on traditional databases in which re-scanning the transactions is allowed. These methods are not useful for mining in data streams (DS) because it is not possible to re-scan the transactions duo to huge and continues data in DS. In this paper, we proposed an effective approach to mining frequent itemsets used for association rule mining in DS named GRM 1 . Unlike other semi-graph methods, our method is based on graph structure and has the ability to maintain and update the graph in one pass of transactions. In this method data storing is optimized by memory usage criteria and mining the rules is done in a linear processing time.Efficiency of our implemented method is compared with other proposed method and the result is presented.