Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...
Online advertising is a major source of revenues in the Internet. In this paper, we identify a number of vulnerabilities of current ad serving systems. We describe how an adversary can exploit these vulnerabilities to divert part of the ad revenue stream f ...
The World Wide Web is one of the most widely used information resources. Understanding the web better will enable us to benefit more of it. In this thesis we develop techniques to learn the properties of the web pages like language and topic using only the ...
We investigate the usability of the Artificial Immune Systems (AIS) approach for solving selected problems in computer networks. Artificial immune systems are created by using the concepts and algorithms inspired by the theory of how the Human Immune Syste ...
By leveraging crowdsourcing, Web credibility evaluation systems (WCESs) have become a promising tool to assess the credibility of Web content, e.g., Web pages. However, existing systems adopt a passive Way to collect users' credibility ratings, which incur ...
Network administrators are faced with a large amount of network data that they need to sift through to analyze user behaviors and detect anomalies. Through a network monitoring tool, we obtained TCP and UDP connection records together with additional infor ...
We consider unicast equation-based rate control, where, at some points in time, a sender adjusts its rate to f(p,r), where p is an on-line estimate of the loss-event rate observed by this source, r of the average round-trip time, and f is a TCP throughput ...
