Formation flying (FF) is a critical element in NASA's future deep-space missions. Terrestrial Planet Finder (TPF), NASA's first space-based mission to directly observe planets outside our own solar system, will rely on FF to achieve the functionality and benefits of a large instrument using multiple lower cost smaller spacecraft. Many key network design problems for such FF missions can be formulated as optimization problems with local and global constraints. We develop a topology control algorithm that can be used for many network problems in the presence of local constraints, such as collision avoidance, and global constraints, such as network connectivity. The presence of contradictory objectives in topology control problems motivated a game-theoretic approach. We demonstrated that a game-theoretic technique could provide a framework for design and analysis of many topology control problems in dynamic networks. In particular, the problem of motion planning for formation reconfiguration in the presence of constraints on network connectivity and inter-spacecraft collisions is studied.

We present new results from our most recent efforts in applying XCS to automatic target recognition (ATR). We place particular emphasis on ATR as a series of linked problems, which include pre-processing of multi-spectral data, detection of objects (in this case, vehicles) in that data, and identification (classification) of those objects. Multi-spectral data contains visual imagery, and additional imagery from several infrared spectral bands. The performance of XCS, with robust features, notably exceeds that of a template-based classifier on the pre-processed multi-spectral data for vehicle identification.

This paper presents the visual threat awareness (VISTA) system for real time collision obstacle detection for an unmanned air vehicle (UAV). Computational stereo performance has progressed such that several commercial or open source implementations are available which operate at frame rate, but suffer from well known correspondence errors. We show that introducing a global segmentation step after commodity stereo can increase robustness and leverage existing stereo software. The global segmentation step is based on a graph structure appropriate for collision detection, human vision inspired foveation, perceptual organization and graph partitioning using the minimum s-t graph cut. This system has been prototyped using the Sarnoff Acadia I vision processor to enable processing of 640 times 480 resolution imagery at 5-10 Hz operation on embedded avionics. We describe system theory, demonstrate segmentation results on scenes of increasing complexity, and show flight experiment results on Georgia Tech's GT-Max autonomous helicopter against real collision obstacles

An efficient method for the generation of collision free, energy optimal, reconfiguration trajectories with energy equalization constraints of formation flying is presented. The main idea is to introduce a set of way-points through which the spacecraft are required to pass combined with certain parameterizations of the trajectories. The resulting energy optimal with equalization and collision avoidance constraints problem is formulated as a parameter optimization problem in terms of the way-points parameters. A numerical algorithm is proposed and used for reconfiguration scenarios involving multiple spacecraft.

In many applications of interest one is faced with the problem of identifying precursor events for extraordinary phenomena. We investigate this problem within the framework of Temporal Data Mining. The concept of Precursor Rule is defined in terms of events and sequences of events. Precursor Rules relate Precursor Events extracted from input time series with Phenomenon events extracted from output time series. A methodology is proposed for extracting Precursor Rules from databases containing time series related to different regimes of a system. Given a fixed output time series containing one or more Phenomenon events, a key contribution of this paper is to show that the Granger Causality Test (GCT) can be used for ranking candidate time series according to the likelihood that Precursor Rules exist. Time Series Quantization is performed for extracting Phenomenon events and Precursor events, but GCT is applied to the raw time series, before Quantization and the definition of Event Types. The paper presents an analytic investigation of the utilization of the GCT for time series pairs containing impulsive time-localized structure. Following a number of approximations, the Granger Causality Index is related with the confidence of the Precursor Rules extracted from these time series pairs. An example from Network Security illustrates the effectiveness of the methodology. Using MIB (Management Information Base) datasets collected from real experiments involving Distributed Denial of Service Attacks, it is shown that Precursor Rules relating activities at Attacking Machines with Traffic Floods at Target Machines can be extracted by the method.

We propose a methodology for utilizing network management systems for the early detection of distributed denial of service (DDoS) attacks. Although there are quite a large number of events that are prior to an attack (e.g. suspicious log-ons, start of processes, addition of new files, sudden shifts in traffic, etc.), in this work we depend solely on information from MIB (management information base) traffic variables collected from the systems participating in the attack. Three types of DDoS attacks were effected on a research test bed, and MIB variables were recorded. Using these datasets, we show how there are indeed MIB-based precursors of DDoS attacks that render it possible to detect them before the target is shut down. Most importantly, we describe how the relevant MIB variables at the attacker can be extracted automatically using statistical tests for causality. It is shown that statistical tests applied in the time series of MIB traffic at the target and the attacker are effective in extracting the correct variables for monitoring in the attacker machine. Following the extraction of these key variables at the attacker, it is shown that an anomaly detection scheme, based on a simple model of the normal rate of change of the key MIBs can be used to determine statistical signatures of attacking behavior. These observations suggest the possibility of an entirely automated procedure centered on network management systems for detecting precursors of distributed denial of service attacks, and responding to them.

Examines the application of statistical traffic modeling for detecting novel attacks against computer networks. We discuss the application of network activity models and application models using the 1998 DARPA Intrusion Detection Evaluation data set. Network activity models monitor the volume of traffic in the network, while application models describe the operation of application protocols. By plotting the ROC (receiver operating characteristic) curves induced by the traffic activity, we quantify the effectiveness of network activity models in discriminating normal connections from attack connections generated by denial-of-service and probing attacks. It is verified that denial-of-service and probing attacks leave traces on simple network activity models, with rates of false alarm which are comparable to the false alarm rates obtained by the participants of the 1998 DARPA evaluation, in which much more complex detection schemes were utilized. For application models, we use the Kolmogorov-Smirnov test to show that attacks using telnet connections in the DARPA data set form a population which is statistically different from the normal telnet connections. The statistics used in our study are the number of bytes from the responder and the responder-originator byte ratio. Again, our results are comparable to those obtained in the DARPA evaluation.