ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

Buffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profiles

Fuzzy ARTMAP has to date been applied to a variety of automatic target recognition tasks, including radar range profile classification. In simulations of this task, it has demonstrated significant compression compared to k-nearest-neighbor classifiers. During supervised learning, match tracking search allocates memory based on the degree of similarity between newly encountered and previously encountered inputs, regardless of their prior predictive success. Here we invesetigate techniques that buffer reset based on a category's previous predictive success and thereby substantially improve the compression achieved with minimal loss of accuracy.Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409, N00014-96-1-0659

ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

Threshold Determination for ARTMAP-FD Familiarity Discrimination

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). ARTMAP-FD quantifies the familiarity of a test pattern by computing a measure of the degree to which the pattern's components lie within the ranges of values of training patterns grouped in the same cluster. This familiarity measure is compared to a threshold which can be varied to generate a receiver operating characteristic (ROC) curve. Methods for selecting optimal values for the threshold are evaluated. The performance of validation-set methods is compared with that of methods which track the development of the network's discrimination capability during training. The techniques are applied to databases of simulated radar range profiles.Advanced Research Projects Agency; Office of Naval Research (N00011-95-1-0657, N00011-95-0109, NOOOB-96-0659); National Science Foundation (IRI-94-01659

Reduction of Natural Killer but Not Effector CD8 T Lymphoyctes in Three Consecutive Cases of Severe/Lethal H1N1/09 Influenza A Virus Infection

Background: The cause of severe disease in some patients infected with pandemic influenza A virus is unclear. Methodology/Principal Findings: We present the cellular immunology profile in the blood, and detailed clinical (and postmortem) findings of three patients with rapidly progressive infection, including a pregnant patient who died. The striking finding is of reduction in natural killer (NK) cells but preservation of activated effector CD8 T lymphocytes; with viraemia in the patient who had no NK cells. Comparison with control groups suggests that the reduction of NK cells is unique to these severely ill patients. Conclusion/Significance: Our report shows markedly reduced NK cells in the three patients that we sampled and raises the hypothesis that NK may have a more significant role than T lymphocytes in controlling viral burden when the host is confronted with a new influenza A virus subtype

ARTMAP-FD: familiarity discrimination applied to radar target recognition

ARTMAP-FD extends fuzzy ARTMAP to perform famaliarity discrimination. That is, the network learns to abstain from meaningless guesses on patterns not belonging to a class represented in the training set. ARTMAP-FD can also be applied in conjunction with sequential evidence accumulation. Its performance is illustrated here on simulated radar range profile data.

Detecting Flood-based Denial-of-Service Attacks with SNMP/RMON ∗ Abstract

We present our work in detecting DoS attacks through the polling of Remote Monitoring (RMON) capable devices. Rather than the introduction of special purpose hardware, our detection capability relies upon RMON capabilities present in existing infrastructure network devices, such as switches and routers. RMON is a special purpose Management Information Base (MIB) designed for the SNMP (Simple Network Management Protocol), which tracks low-level network usage indicators, such as byte and packet count, packet size, and packet transmission error events. Using RMON data polled from a live enterprise network, we have developed a detection algorithm for simulated flood-based DoS attacks that achieves a high detection rate and low false alarm rate. The detection algorithm relies not only on the raw RMON variables but also on relationships between the variables to achieve this detection rate. We also indicate how the introduction of RMON2 variables and an accurate network map can be used to improve DoS detection accuracy and reduce false alarms by identifying the sources of specific DoS-related traffic. Our approach is less expensive than many commerically available solutions, requiring no special purpose hardware. It is more accurate than commonly used univariate statistical approaches and it is fast, requiring only the computation of packet variables ratios and processing by a feed-forward neural network. 1