Centralized, measurement-based, spectrum management for environments with heterogeneous wireless networks

Abstract—Heterogeneity of wireless networks has become an increasing problem in the wireless spectrum that breaks down spectrum sharing and exacerbates interference. Many coexistence techniques have been proposed to alleviate this interference, however, they are difficult to deploy due to changes needed in the protocols, overhead, and rapid changes in technology. In this paper, we focus on the potential of spectrum man-agement to provide a long-term solution. We introduce novel components to a spectrum management system that overcomes limitations of current models that have remained relatively focused on homogeneous environments. Our approach is a centralized one, where we analyze information collected from heterogeneous monitors available today, structure the information in a hypergraph, and perform an analysis to detect heterogeneous conflicts. Introducing a mixed integer program (in addition to other novel components), we reconfigure devices in the spectrum to avoid conflicts and improve performance. I

An Empirical Evaluation of Entropy-based Anomaly Detection

There is considerable interest in using entropy-based analysis of traffic feature distributions for anomaly detection. Entropy-based metrics are appealing since they provide more fine-grained insights into traffic structure than traditional traffic volume analysis. While previous work has demonstrated the benefits of using the entropy of different traffic distributions in isolation to detect anomalies, there has been little effort in comprehensively understanding the detection power provided by entropy-based analysis of multiple traffic distribution used in conjunction with each other. We compare and contrast the anomaly detection capabilities provided by different entropybased metrics. We consider two classes of distributions: flow-header features (IP addresses, ports, and flow-sizes), and behavioral features (out- and in-degree of hosts measuring the number of distinct destination/source IP addresses that each host communicates with). Somewhat surprisingly, we observe that the entropy of the address and port distributions are strongly correlated with each other, and also detect very similar anomalies in our traffic trace. The behavioral and flow size distributions appear less correlated and detect incidents that do not show up as anomalies amon