This research focuses on the input shape of CNN-based application traffic. The previously proposed multi-input model CNN classification method classified applications through various shapes of features derived from fixed-length packets, achieving a higher classification accuracy compared to traditional CNNs. However, it had limitations such as vulnerability to overfitting despite its high classification accuracy and slow inference speed. To overcome these challenges, we introduce a lightweight version of the previously proposed MISCNN, called MISCNN+. MISCNN+ demonstrated approximately 2.9 times faster inference speed and a 3.6% improvement in classification accuracy compared to the previous version.

SaaS is a cloud-based application service that allows users to use applications that work in a cloud environment. SaaS is a subscription type, and the service expenditure varies depending on the license, the number of users, and duration of use. For efficient network management, security and cost management, accurate detection of user behavior for SaaS applications is required. In this paper, we propose a rule-based traffic analysis method for the user behavior detection. We conduct comparative experiments with signature-based method by using the real SaaS application and demonstrate the validity of the proposed method.

Since the development of Bitcoin, the first blockchain-based cryptocurrency, many cryptocurrencies have formed and have traded in markets. The integrity and anonymity of cryptocurrency was enough to raise its value and its price gained worldwide attention. Therefore, many studies are being carried out to predict the price of cryptocurrency for make a profit. We cluster time series through K-Medoids algorithm and train and evaluate each cluster with predictive models. We also examine the predictive performance in Bitcoin price according to the various distance measurement of clustering.