No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis

MINING ACTIONABLE INTENTS IN QUERY ENTITIES

Understanding search engine users’ intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users’ future actions. In this paper, we present a novel research for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, i.e. the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment based on the Action Mining (AM) query entity dataset from Actionable Knowledge Graph (AKG) task at NTCIR-13 suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Automated information retrieval and services of graduate school using chatbot system

Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research