Classification of diversified web crawler accesses inspired by biological adaptation
by Naomi Kuze; Shu Ishikura; Takeshi Yagi; Daiki Chiba; Masayuki Murata
International Journal of Bio-Inspired Computation (IJBIC), Vol. 17, No. 3, 2021

Abstract: To discover and prevent attacks, it is necessary to collect data about the attacks using honeypots and to identify malicious accesses from collected data. In this study, we focus on detecting a massive number of crawler accesses, which complicates the detection of malicious accesses. We adapt AntTree, a bio-inspired clustering scheme that is highly scalable and adaptable, for crawler detection. We also designed a feature vector for crawler detection and propose a cluster interpretation method of AntTree. Our results show that the proposed bio-inspired mechanism can detect crawlers with a low false-negative rate, which is an advantage over conventional schemes for detecting various types of crawler.

Online publication date: Mon, 10-May-2021

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