23300308
Social networks: looking ahead
By now, online social networks have become an indispensable part of both online and offline lives of human beings. A large fraction of time spent online by a user is directly influence by the social networks to which he/she belongs. This calls for a deeper examination of social networks as large-scale dynamic objects that foster efficient person-person interaction.
 The goal of our panel is to discuss social networks from various research angles. In particular, we plan to focus on the following broad research-related topics: large scale data mining, algorithmic questions, sociological aspects, privacy, web search, etc. We will also discuss the business and societal impacts of social networks. Each of these topics has generated a lot of research in recent years and while taking stock of what has been done, we will also be discussing the directions in which these topics are headed, from both science and society points of view. Our panel will consist of eminent researchers, who have worked/been working on an eclectic and diverse mix of problems in social networks
11285733
Privacy Vulnerabilities in Encrypted HTTP Streams
Encrypting traffic does not prevent an attacker from performing some types of traffic analysis. We present a straightforward traffic analysis attack against encrypted HTTP streams that is surprisingly effective in identifying the source of the traffic. An attacker starts by creating a profile of the statistical characteristics of web requests from interesting sites, including distributions of packet sizes and inter-arrival times. Later, candidate encrypted streams are compared against these profiles. In our evaluations using real traffic, we find that many web sites are subject to this attack. With a training period of 24 hours and a 1 hour delay afterwards, the attack achieves only 23% accuracy. However, an attacker can easily pre-determine which of trained sites are easily identifiable. Accordingly, against 25 such sites, the attack achieves 40% accuracy; with three guesses, the attack achieves 100% accuracy for our data. Longer delays after training decrease accuracy, but not substantially. We also propose some countermeasures and improvements to our current method. Previous work analyzed SSL traffic to a proxy, taking advantage of a known flaw in SSL that reveals the length of each web object. In contrast, we exploit the statistical characteristics of web streams that are encrypted as a single flow, which is the case with WEP/WPA, IPsec, and SSH tunnels.
12992442
Relational data pre-processing techniques for improved securities fraud detection
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarely structured appropriately for knowledge discovery, and they often contain variables whose meanings change across different subsets of the data. We describe how these challenges were addressed in a collaborative analysis project undertaken by the University of Massachusetts Amherst and the National Association of Securities Dealers(NASD). We describe several methods for data pre-processing that we applied to transform a large, dynamic, and relational dataset describing nearly the entirety of the U.S. securities industry, and we show how these methods made the dataset suitable for learning statistical relational models. To better utilize social structure, we first applied known consolidation and link formation techniques to associate individuals with branch office locations. In addition, we developed an innovative technique to infer professional associations by exploiting dynamic employment histories. Finally, we applied normalization techniques to create a suitable class label that adjusts for spatial, temporal, and other heterogeneity within the data. We show how these pre-processing techniques combine to provide the necessary foundation for learning high-performing statistical models of fraudulent activity.
543844
Supply chain simulation: distributed supply chain simulation using a generic job running framework
For supply chain performance simulation that involves aggregating results from multiple runs of the same underlying model, simulation iterations can be distributed to networked computing resources to achieve significant speedup. This paper presents a generic distributed job running framework that facilitates such high performance supply chain simulation. We first introduce a supply chain modeling and simulation tool developed by IBM Research, and summarize the strategy to enhance it. A closer look is then taken at a generic job running framework we designed and how it was used to bring the distributed simulation capability to the tool. After reviewing an ongoing effort to integrate the new tool with the IBM MathGrid environment, we conclude the paper with a brief discussion of our future work.
69276
Fast and efficient voltage scheduling by evolutionary slack distribution
To minimize energy consumption by voltage scaling in design of heterogeneous real-time embedded systems, it is necessary to perform two distinct tasks: task scheduling (TS) and voltage selection (VS). Techniques proposed to date either are fast but yield inefficient results, or output efficient solutions after many slow iterations. As a core problem to solve in the inner loop of a system-level optimization cycle, it is critical that the algorithm be fast while producing high quality results. This paper presents a new technique called Evolutionary Relative Slack Distribution Voltage Scheduling (ERSD-VS) that achieves both speed and efficiency. It addresses priority adjustment and slack distribution issues with low cost heuristics. Experimental results from running publicly available testbenches show up to 42% energy saving compared to a published technique called EVEN-VS. It also shows up to 70 times speed improvement compared to an efficient technique called EE-GLSA.