In order to understand the overall picture of cyber attacks and to identify the source of cyber attacks, a method to identify malicious activities by automatically creating a graph that ties together the dependencies of a series of related events by tracking Data Provenance has been developed. However, the problem of dependency explosion, in which a large number of normal computer system operations such as operations by authorized users are included in the dependencies, results in a huge generated graph, making it difficult to identify malicious activities. In this paper, we propose a method to reduce the search space for malicious activities by extracting and removing frequently occurring benign activities through natural language processing of log data and analysis of activities in the computer system using similarity judgments. In the evaluation experiment, we used the DARPA TC Dateset, a large-scale public dataset, to evaluate the effectiveness of the proposed method on the dependency explosion problem. In addition, we showed that about 6.8 to 39% of the activities in a computer system could be defined as patterns of benign activities. In addition, we showed that removing benign activities extracted from a portion of the log data (approximately 1.4% to 3.2% in size) can significantly reduce the search space (up to approximately 52%) in large data sets.