This tutorial provides an introduction to privacy problems in mobile applications, and explains also how Prerequisites to attend this tutorial are basic knowledge of application security and understanding of Solution that verifies the integrity and confidentiality of the data managed by the program itself.The only two
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This long tutorial presents both static and dynamic analysis approaches to enforce privacy of mobile applications,Īnd includes a hands-on lab that teaches the audience how to use an open-source tool to create a static-analysis Static analysis is a very promising solution but suffers from the dual problem of false positives. The possible paths of execution of an application are tested under all the possible inputs, and so false negatives Testing is also limited because there is often no guarantee that all
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On manual code inspection is unrealistic. Detecting security problems in today's mobile applications by just relying
Watson Research Center, USA)Ībstract:Program analysis has become an essential tool to verify the correctness of programs before these are deployed Tutorial 2: Program Analysis for Mobile Application Integrity and Privacy Enforcement Lecturer: The US Army Research Office, DARPA, and a gift from Northrop Grumman Aerospace Systems. Her research is currently supported by the National Science Foundation, Recipient of the NSF CAREER award (2015) and Army Research Office Young InvestigatorĪward (2013). Research has won 4 publication awards Best Research Paper at SIAM SDM 2015, Best PaperĪt ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML/PKDD 2009. Problems arising in graph mining, pattern discovery, social and information networks,Īnd especially anomaly mining outlier, fraud, and event detection. Wide range of data mining and machine learning topics with a focus on algorithmic
Labs and Microsoft Research at Redmond during summers. from the Computer Science DepartmentĪt Carnegie Mellon University in 2012. Leman Akoglu is an Assistant Professor in the Department of Computer Science at With this intuition for how these methods are applied to graphs and user behavior, we will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters. We will then give brief examples of recent research using the techniques to model, understand and predict normal behavior. the "who-reviews-what" graphs of Amazon and Yelp.įor each of these techniques we will give an explanation of the algorithms and the intuition behind them. "who-calls-whom" networks, and attributed graphs, e.g. In particular, we will focus on three data mining techniques: subgraph analysis, label propagation and latent factor models and their application to static graphs, e.g. Christos Faloutsos (Carnegie Mellon University, USA)Ībstract:How do anomalies, fraud, and spam effect our models of normal user behavior? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection.Leman Akoglu (Stony Brook University, USA).Alex Beutel (Carnegie Mellon University, USA).Tutorial Abstracts and Lecturer Resumes Tutorial 1: Fraud Detection through Graph-Based User Behavior Modeling Lecturers: