From Map to Dist: the Evolution of a Large-Scale Wlan Monitoring System

The edge of the Internet is increasingly becoming wireless. Therefore, monitoring the wireless edge is important to understanding the security and performance aspects of the Internet experience. We have designed and implemented a large-scale WLAN monitoring system, the Distributed Internet Security Testbed (DIST), at Dartmouth College. It is equipped with distributed arrays of “sniffers” that cover 210 diverse campus locations and more than 5,000 users. In this paper, we describe our approach, designs and solutions for addressing the technical challenges that have resulted from efficiency, scalability, security, and management perspectives. We also present extensive evaluation results on a production network, and summarize the lessons learned

Analysis of a SCADA System Anomaly Detection Model Based on Information Entropy

SCADA (supervisory control and data acquisition) systems monitor and control many different types of critical infrastructure such as power, water, transportation, and pipelines. These once isolated systems are increasingly being connected to the internet to improve operations, which creates vulnerabilities to attacks. A SCADA operator receives automated alarms concerning system components operating out of normal thresholds. These alarms are susceptible to manipulation by an attacker. This research uses information theory to build an anomaly detection model that quantifies the uncertainty of the system based on alarm message frequency. Several attack scenarios are statistically analyzed for their significance including someone injecting false alarms or hiding alarms. This research evaluates the use of information theory for anomaly detection and the impact of different attack scenarios