Bot recognition in a Web store: An approach based on unsupervised learning Abstract   Web traffic on ebusiness sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable ecustomer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to ecommerce. The approach studied in this work explores the use of unsupervised learning (kmeans and Graded Possibilistic cMeans), followed by supervised labelling of clusters, a generative learning strategy that decouples modelling the data from labelling them. Its efficiency is evaluated through experiments on real ecommerce data, in realistic conditions, and compared to that of supervised learning classifiers (a multilayer perceptron neural network and a support vector machine). Results demonstrate that the classification based on unsupervised learning is very efficient, achieving a similar performance level as the fully supervised classification. This is an experimental indication that the bot recognition problem can be successfully dealt with using methods that are less sensitive to mislabelled data or missing labels. A very small fraction of sessions remain misclassified in both cases, so an indepth analysis of misclassified samples was also performed. This analysis exposed the superiority of the proposed approach which was able to correctly recognize more bots, in fact, and identified more camouflaged agents, that had been erroneously labelled as humans.

Inquiry Spam Detection via Jointly Exploiting TemporalCategorical Behavior and Linguistics Inquiry performs one of the backbones in current Ecommerce websites. Detecting spams in inquiries is essential for these platforms but is surprisingly underexplored by current research. In this paper, we propose a system coined ISTBEL to detect spam inquiries. Motivated by the observations on both behavioral and linguistic differences between spammers and benign users, ISTBEL jointly utilizes the temporalcategorical behavioral sequence and text sequence to accomplish its purpose. At its heart, a variant of LSTM equipped interactive attentions is devised to strengthen the coherence between the categorical and temporal behaviors. A characterlevel CNN is adopted to capture the capricious linguistic patterns. These two modules are integrated and collaboratively contribute to the final predictions. Our system is trained through an endtoend manner and can directly utilize raw data as input without tedious feature engineering. We have applied the system to inquiry spam detection on Alibaba.com, and results show ISTBEL is promising in detection spammers and outperforms baselines by a large margin. Meanwhile, ISTBEL can be easily applied to other applications which contain both behavioral and linguistic information.