Abstract
Web bots are malicious scripts that automatically traverse the websites, fill the web form and illegally scrap the data from web sites. The never-ending threat of web bot is causing serious problems on the web applications. According to various web bot traffic reports, more than fifty percent of the total web traffic is coming from web bots. An effective safeguard against automated web bots is to detect the human user presence on the web applications. Most part of the existing research is focused on specific web bot detection such as form spamming bot, data scrapping bots, chat bots, and game bots. In this paper, the web bot detection model is proposed using combined supervised and unsupervised machine learning algorithms. In this paper, new Biostatistics features are proposed which is used to identify the human user presence on web applications. The Biostatistics features have proven very effective in discriminating human users from general web bots. Various attack scenarios are created for web bot attacks such as automated account registration, automatic form filling, and data scrapping to mimic the zero-day web bot attacks. The proposed model is evaluated by numerous experiments using standard evaluation parameters. The result analysis reveals that the proposed model is efficient in discriminating human users from web bots.
Published Version
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