Barking Up the Right Tree: Are Small Groups Rational Agents?

Both mainstream economics and its critics have focused on models of individual rational agents even though most important decisions are made by small groups. Little systematic work has been done to study the behavior of small groups as decision-making agents in markets and other strategic games. This may limit the relevance of both economics and its critics to the objective of developing an understanding of how most important decisions are made. In order to gain some insight into this issue, this paper compares group and individual economic behavior. The objective of the research is to learn whether there are systematic differences between decisions made by groups and individual agents in market environments characterized by risky outcomes. A quantitative measure of deviation from minimallyrational decisions is used to compare group and individual behavior in common value auctions.

Team Composition, Knowledge and Collaboration

This paper explores the effects of knowledge homogeneity (shared or common) and knowledge heterogeneity (distributed) on team outcomes and processes. An experiment was conducted in which teams made resource allocation decisions while physically dispersed and supported with a shared virtual work surface and chat. The task required teams to learn and recognize patterns and then collaborate to allocate their resources appropriately. Dependent measures included process (chat, movement, conflict), and outcome quality. All teams received significant financial rewards in direct proportion to their performance. Teams with common knowledge significantly outperformed teams with distributed knowledge. Heterogeneous teams appeared to use the leader/follower paradigm

Comparison of Supervised and Unsupervised Learning for Detecting Anomalies in Network Traffic

Adversaries are always probing for vulnerable spots on the Internet so they can attack their target. By examining traffic at the firewall, we can look for anomalies that may represent these probes. To help select the right techniques we conduct comparisons of supervised and unsupervised machine learning on network flows to find sets of outliers flagged as potential threats. We apply Functional PCA and K-Means together versus Multilayer Perceptron on a real-world dataset of traffic prior to an NTP DDoS attack in January 2014; scanning activity was heightened during this pre-attack period. We partition data to evaluate detection powers of each technique and show that FPCA+Kmeans outperforms MLP. We also present a new variation of the circle plot for visualization of resulting outliers which we suggest excels at displaying multidimensional attributes of an individual IP\u27s behavior over time. In small multiples, circle plots show a gestalt overview of traffic

An Unsupervised Approach to DDoS Attack Detection and Mitigation in Near-Real Time

We present an approach for Distributed Denial of Service (DDoS) attack detection and mitigation in near-real time. The adaptive unsupervised machine learning methodology is based on volumetric thresholding, Functional Principal Component Analysis, and K-means clustering (with tuning parameters for flexibility), which dissects the dataset into categories of outlier source IP addresses. A probabilistic risk assessment technique is used to assign “threat levels” to potential malicious actors. We use our approach to analyze a synthetic DDoS attack with ground truth, as well as the Network Time Protocol (NTP) amplification attack that occurred during January of 2014 at a large mountain-range university. We demonstrate the speed and capabilities of our technique through replay of the NTP attack. We show that we can detect and attenuate the DDoS within two minutes with significantly reduced volume throughout the six waves of the attack

INTEGRATION OF INFORMATION SYSTEMS TECHNOLOGIES TO SUPPORT CONSULTATION IN AN INFORMATION CENTER

This paper presents an approach for integrating different types of information systems technologies to support the functions of an Information Center (IC). A knowledge based system, Information Center Expert/Help Service (ICE/H), has been developed to provide support for the help services of an IC. A general process model to represent the consultation process in an IC is described. Based on this model, an architecture to support the consultation process has been developed. The architecture depicts the use of a knowledge management system, a data management system and a communication (E-mail) system to emulate the consultation process. The ICE/H system has been implemented using this architecture to support an IC with 5000 users

Integrating multiple knowledge bases within Google Desktop

Google is clearly the preferred solution when searching for information. However, how does one search for information within proprietary knowledge bases? We believe that we can use the power of Google Desktop (GD) as a repository and search mechanism for local proprietary knowledge. We have constructed a multi-socketed server (using C++) which allows network clients explicit control of textual input to GD. Using the ATL/COM interface, our software registers as a GD plug-in. Our server supports GD API "events", such as note, email, and instant message, and processes events at approximately 10/s. To test the effectiveness of the system, we downloaded 600,000 posts from a popular, public threaded discussion forum and 150,000 posts from a subscription-based forum. Our server partitions these knowledge sets so that they can be independently searched. We found that when compared to forum string search functions, our partitioned GD search tool produced significantly superior results.CableLabs Favorite