Detection and Discrimination of Injected Network Faults

Abstract Although the present work does in fact employ training data, it does so in the interest of calibrating the results Six hundred faults were induced by injection into five live obtained from an experimental detection and diagnostic campus networks at Carnegie Mellon University in order system designed specifically to accommodate noisy, to determine whether or not particular network faults nonstationary, nonspecific domains. The system have unique signatures as determined by out-of-band generalizes by virtue of its log analysis capabilities; all monitoring instrumentation. If unique signatures span monitored data and events are recorded in log files. networks, then the monitoring instrumentation can be These files are processed by the system, resulting in used to diagnose network faults, or distinguish among testable and reproducible detections and diagnoses of fault classes, without human intervention, using anomalous conditions. Any monitored process or machine-generated diagnostic decision rules. This device can be used to populate the logs with data. would be especially useful in large, unmanned systems in which the occurrence of novel or unanticipated faults The specific objective of the present work is to conduct could be catastrophic. Results indicate that significant a designed experiment to test the detection and diagaccuracy in automated detection and discrimination nosis capabilities of a system for handling faults in local among fault types can be obtained using anomaly sigarea networks. Networks were selected as a test natures as described here. domain because their operating characteristics include nonlinear, nonstationary dynamic behavior. The experiment uses automated injection techniques to induc

The Female Lower Genital Tract Is a Privileged Compartment with IL-10 Producing Dendritic Cells and Poor Th1 Immunity following Chlamydia trachomatis Infection

While a primary genital tract infection with C. trachomatis stimulates partial-protection against re-infection, it may also result in severe inflammation and tissue destruction. Here we have dissected whether functional compartments exist in the genital tract that restrict Th1-mediated protective immunity. Apart from the Th1-subset, little is known about the role of other CD4+ T cell subsets in response to a genital tract chlamydial infection. Therefore, we investigated CD4+ T cell subset differentiation in the genital tract using RT-PCR for expression of critical transcription factors and cytokines in the upper (UGT) and lower genital tract (LGT) of female C57BL/6 mice in response to C. trachomatis serovar D infection. We found that the Th1 subset dominated the UGT, as IFN-γ and T-bet mRNA expression were high, while GATA-3 was low following genital infection with C. trachomatis serovar D. By contrast, IL-10 and GATA-3 mRNA dominated the LGT, suggesting the presence of Th2 cells. These functional compartments also attracted regulatory T cells (Tregs) differently as increased FoxP3 mRNA expression was seen primarily in the UGT. Although IL-17A mRNA was somewhat up-regulated in the LGT, no significant change in RORγ-t mRNA expression was observed, suggesting no involvement of Th17 cells. The dichotomy between the LGT and UGT was maintained during infection by IL-10 because in IL-10-deficient mice the distinction between the two compartments was completely lost and a dramatic shift to the predominance of Th1 cells in the LGT occurred. Unexpectedly, the major source of IL-10 was CD11c+ CD11b+ DC, probably creating an anti-inflammatory privileged site in the LGT

Comparing Anomaly-Detection Algorithms for Keystroke Dynamics

Keystroke dynamics—the analysis of typing rhythms to discriminate among users—has been proposed for detecting impostors (i.e., both insiders and external attackers). Since many anomaly-detection algorithms have been proposed for this task, it is natural to ask which are the top performers (e.g., to identify promising research directions). Unfortunately, we cannot conduct a sound comparison of detectors using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. We collected data from 51 subjects typing 400 passwords each, and we implemented and evaluated 14 detectors from the keystrokedynamics and pattern-recognition literature. The three top-performing detectors achieve equal-error rates between 9.6 % and 10.2%. The results—along with the shared data and evaluation methodology—constitute a benchmark for comparing detectors and measuring progress. 1