CDM Colloquium – PhD talks

Colloquium starts Friday, September 15th with two talks from out PhD students presenting their research in network security and in recommender systems. The abstract and bio of the talks are as below. Click here for event details.

User-Targeted Denial of Service attacks – Rami Ghannam (DePaul University)

Abstract: Mobile networks are prevalent in today’s world, being used in a variety of applications ranging from personal use to the work environment and other. Ensuring security for users in a mobile network is therefore increasingly important. Denial-of-service attacks or DoS proved to be the biggest threat to mobile networks in recent years. A lot of work has been done in DoS targeting the infrastructure of the mobile network. User-targeted DoS attacks have been neglected in comparison. The fourth generation of cellular networks 4G LTE is the fastest growing mobile network in terms of subscriber numbers. The security aspect of mobile networks has improved throughout the generations, however, 4G proved to still have vulnerabilities in the signaling plane that allow a malicious attacker to target a specific user. In particular, the Attach Request procedure and the Tracking Area Update (TAU) procedure could be exploited. Deploying a rogue base station and forcing the targeted user to connect to it is therefore possible. The attacker could then deny selected services of the targeted user such as LTE data communication.

Bio: Rami Ghannam has a Bachelor’s and Master’s degree in Computer and Communications Engineering. He worked for two years as a network software engineer conducting the installation and quality control process for PBX platforms. He joined DePaul University in 2012 to pursue his PhD in Computer Science. His research interests include Computer Communications Networks, Software Defined Networks and Mobile Networks.

Effective Exploration Exploitation Trade-off in Sequential Music Recommendation – Himan Abdollahpouri (DePaul University)

Abstract: Personalization is an essential part of the recommender systems. That is, tailoring the recommended items based on the tastes and interests of the end user. However, in many real world applications, there is a tremendous need to explore a broader range of items for a variety of reasons such as finding out more about what a user would like and what s/he wouldn’t, giving the opportunity to different items to be exposed to users and, lack of available items that match the user’s immediate preferences, to name just a few.

Music recommendation has become very popular in recent years due to its great performance in terms of helping users to find interesting songs in an easy-to-use manner. Similar to many other recommendation domains, explorations is very important in music recommender systems as it allows the system to learn more about the user and, at the same time, achieve more information about certain items that have not been rated enough. To the best of our knowledge, exploration in recommender systems has been done mostly at random. That is, there is no timing strategy to do exploration versus exploitation. In this project, we show that the previous sequence of the played content in a sequential music recommendation is important in deciding whether an explore item should be recommended or an exploit one.

Bio: Himan Abdollahpouri is a PhD candidate at the Web Intelligence lab at DePaul University. Himan holds an MSc in Artificial Intelligence and a BSc in Computer Engineering from Iran University of Science and Technology and Bu Ali Sina University, respectively. Prior to joining DePaul, Himan was a senior software engineer at TOSAN Inc., the most leading company in software engineering and payment/banking industry in Iran. While in the U.S., Himan did an internship as a data scientist in Summer 2017 at Pandora Media Inc. to help the company improve their user engagement via more efficient music recommendation strategies. Himan has several publications in the most prestigious venues in recommender systems such as ACM RecSys and ACM UMAP. Himan’s research interests primarily lie in machine learning, recommender systems, and data mining. In addition, he is also interested in psychology and sociology and how these two could be combined with machine learning to have better user modeling.

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