on methods are rule based, where rules between network logs and failure modes are set manually in advance. By trapping a log, we can detect a corresponding network failure. However, much progress has been made recently in software-based network function virtualization, which means that the network configuration is dynamically changing. Consequently, building those rules and updating them are difficult and time-consuming tasks. In addition, because of the increasing network complexity and the growing number of network Downloaded: 4/15/2022 9:44:49 AM Nova Southeastern University IP Address: 137.52.76.29 Learning With Software-Defined Area roles, the monitoring information (network logs) obtained using existing methods is insufficient for monitoring the network status. To solve this problem, we are working on enhancing network monitoring and improving the accuracy of failure detection. Current network monitoring techniques are based on the use of internal network data such as network logs and external network data such as service monitoring and userclaims. To improve the coverage and agility of monitoring, we are implementing failure detection based on SNS data. However, because SNS data consist of free- format text messages and includes a huge number of messages other than those related to network outages; we explained a technique to accurately extract messages that correspond to network failures. In addition, by estimating the locations of such messages, we can estimate the location and the impact of the network failure. Network logs that are used for network monitoring are divided into numerical logs such as CPU (central processing unit) and interface loads, and messages in text format such as syslog messages. Numerical logs are used for detecting network failures by applying predefined thresholds. However, defining thresholds for a huge number of logs is difficult. We apply statistical outlier detection based on a non- supervised machine learning method. In addition, we apply this method to obtain the time seri