Sapienza - Università di Roma
 Computer Science Department

PhD School

Borromini's Lantern (Saint Ivo at La Sapienza)

Advanced Topics in Computer Science Seminars

Coordinators: Prof. Nicola Galesi (email: nicola.galesi[at]uniroma1.it) - Prof. Daniele Venturi (email: venturi[at]di.uniroma1.it)

Spring 2017

Speaker: Raheem Beyah - Georgia Institute of Technology

Date: Tuesday, April 4, 2017

Time: 12:00

Room: Seminari

Title: Toward Maintaining Control of Industrial Control Systems

Abstract: Industrial control system (ICS) networks used in critical infrastructures such as the power grid present a unique set of security challenges. The distributed networks are difficult to physically secure, legacy equipment can make cryptography and regular patches virtually impossible, and compromises can result in catastrophic physical damage. In this talk, I will discuss the expanded attack surface of control systems. Further, I will present two device type fingerprinting methods designed to augment existing intrusion detection methods in the ICS environment. The first method measures data response processing times and takes advantage of the static and low-latency nature of dedicated ICS networks to develop accurate fingerprints, while the second method uses the physical operation times to develop a unique signature for each device type. Additionally, the physical fingerprinting method is extended to develop a completely new class of fingerprint generation that requires neither prior access to the network nor an example target device. Fingerprint classification accuracy is evaluated using a combination of a real world five month dataset from a live power substation and controlled lab experiments. Finally, the efficacy of simple forgery attempts against the proposed methods are investigated.

Bio: Raheem Beyah is the Motorola Foundation Professor and Associate Chair for Strategic Initiatives and Innovation in the School of Electrical and Computer Engineering at Georgia Tech where he leads the Communications Assurance and Performance Group (CAP) and is a member of the Institute for Information Security & Privacy (IISP) and the Communications Systems Center (CSC). Prior to returning to Georgia Tech, Dr. Beyah was an Assistant Professor in the Department of Computer Science at Georgia State University, a research faculty member with the Georgia Tech CSC, and a consultant in Andersen Consulting's (now Accenture) Network Solutions Group. He received his Bachelor of Science in Electrical Engineering from North Carolina A&T State University in 1998. He received his Masters and Ph.D. in Electrical and Computer Engineering from Georgia Tech in 1999 and 2003, respectively. Dr. Beyah has served as a Guest Editor for MONET and is currently an Associate Editor of the (Wiley) Wireless Communications and Mobile Computing Journal. His research interests include network security, wireless networks, network traffic characterization and performance, and critical infrastructure security. He received the National Science Foundation CAREER award in 2009 and was selected for DARPA's Computer Science Study Panel in 2010. He is a member of AAAS, ASEE, a lifetime member of NSBE, a senior member of IEEE, and an ACM Distinguished Scientist.

Speaker: Walter Binder - University of Lugano

Date: Friday, April 28, 2017

Time: 14:00

Room: Seminari

Title: Accurate Profiling in a Virtual Machine with Dynamic Compilation

Abstract: Many profilers based on bytecode instrumentation yield wrong results in the presence of an optimizing dynamic compiler, either due to not being aware of compiler optimizations or due to the inserted code disrupting some optimizations. To avoid such perturbations, we present a technique to make any profiler implemented at the bytecode level aware of optimizations performed by the dynamic compiler. We implement our approach in a state-of-the-art Java virtual machine and evaluate it with several profilers. (Joint work with Yudi Zheng and Lubomir Bulej)

Bio: Walter Binder is an associate professor in the Faculty of Informatics, Università della Svizzera italiana (USI), Switzerland. He holds an MSc, a PhD, and a Venia Docendi from Vienna University of Technology, Austria. His main research interests are in the areas of program analysis, virtual machines, parallel programming, and cloud computing.

Speaker: Stefano Basagni - Northeastern University

Date: Tuesday, May 16, 2017

Time: 12:00

Room: Seminari

Title: Wake-up Radio, Energy Harvesting and Multi-modality! Oh My! (Very Long-lived Wireless Sensing Systems for the IoT.)

Abstract: We will explore the impact of recent technologies on the performance of wireless networked systems that enable the Internet of Things (IoT) in different application scenarios, both terrestrial and underwater.
Specifically, we describe advances in wake-up radio technology with semantic addressing capabilities and its usage to obtain very short packet delivery delays and up to five orders of magnitude energy savings, resulting in lifetimes that are decades longer than those obtained with standard energy-conserving methods (e.g., duty cycling).
We will then illustrate how combining energy harvesting from different sources (solar and wind) with wake-up radios enables perennial networked systems with performance similar to that of systems with battery-operated nodes.
Finally, we will demonstrate that a smartly learned selection of forwarder nodes and communication modality allows us to double packet delivery ratio in demanding underwater IoT scenarios, while maintaining low latencies and energy consumption.

Bio: Stefano Basagni holds a Ph.D. in electrical engineering from the University of Texas at Dallas (December 2001) and a Ph.D. in computer science from the University of Milano, Italy (May 1998). He received his B.Sc. degree in computer science from the University of Pisa, Italy, in 1991. Since Winter 2002 he is on faculty at the Department of Electrical and Computer Engineering at Northeastern University, in Boston, MA, where he is currently associate professor. From August 2000 to January 2002 he was assistant professor of computer science at the Department of Computer Science of the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas.
Dr. Basagni's current research interests concern research and implementation aspects of mobile networks and wireless communications systems, Bluetooth and sensor networking, definition and performance evaluation of network protocols and theoretical and practical aspects of distributed algorithms.
Dr. Basagni has published over five dozens of referred technical papers and book chapters. He is also co-editor of two books. Dr. Basagni served as a guest editor of the special issue of the Journal on Special Topics in Mobile Networking and Applications (MONET) on Multipoint Communication in Wireless Mobile Networks, of the special issue on mobile ad hoc networks of the Wiley's Interscience's Wireless Communications & Mobile Networks journal, of the special issue of the Elsevier's journal Ad Hoc Networks on advances in ad hoc and sensor networking, and of the special issue of the Elsevier's journal Algorithmica on algorithmic aspects of mobile computing and communications.
Dr. Basagni serves as a member of the editorial board and of the technical program committee of ACM and IEEE journals and international conferences. He is a senior member of the ACM (including the ACM SIGMOBILE), senior member of the IEEE (Computer and Communication societies), and member of ASEE (American Society for Engineering Education).

Speaker: Carlotta Domeniconi - George Mason University

Date: Tuesday, May 23, 2017

Time: 12:00

Room: Seminari

Title: Winnowing the Wheat from the Chaff in Complex and Large Data

Abstract: The need for mining massive and complex data has become paramount in areas like education, web mining, social network analysis, security, and a variety of scientific pursuits. However many traditional supervised and unsupervised learning algorithms break down when applied in big data scenarios: this is known as the "big data problem". Among other concerns, big data presents serious scalability difficulties for these algorithms.In this talk I will discuss the big data problem and solutions in the context of complex data I have worked on in recent years, namely, social networks, text, and educational data. I will also present a new method for distributed machine learning which directly tackles key problems posing challenges to successful and scalable mining of big data. Our method is scalable, general-purpose with regard to the machine learning algorithm, and easily adaptable to a variety of heterogeneous grid or cloud computing scenarios.

Bio: Carlotta Domeniconi is an Associate Professor in the Department of Computer Science at George Mason University. Her research interests include machine learning, pattern recognition, educational data mining, and feature relevance estimation, with applications in text mining, bioinformatics, and finance. She has published extensively in premier journals and conferences in machine learning and data mining. She was the program co-Chair of SIAM SDM in 2012 and the co-Chair of a number of workshops, and she is serving as the General Chair for SIAM SDM 2016-17. Dr. Domeniconi has served as PC member and area chair for a variety of top-tier conferences; she was an Associate Editor of the IEEE Transactions of Neural Networks and Learning Systems Journal, and is currently serving on the editorial board of TKDE, KAIS, and Computational Intelligence journals. Dr. Domeniconi is a recipient of an ORAU Ralph E. Powe Junior Faculty Enhancement Award. She has worked as PI or co-PI on projects supported by the NSF, the US Army, the Air Force, the DoD, FINRA, and she is a recipient of an NSF CAREER Award.