James W. Handley

Data modeling for radar applications

This paper presents the process of data modeling for generating functional {[f(x)]/sup n/} models of algorithms or data set relationships in simple polynomial form. Data modeling has been applied to missing data reconstruction, signature synthesis, interpolation, data compression, classifier performance modeling, and adaptive feature selection. This paper highlights the mathematical process and successful applications.

Data modeling of network dynamics

This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.

Data-driven differential equation modeling of fBm processes

This paper presents a unique method for modeling fractional Brownian motion type data sets with ordinary differential equations (ODE) and a unique fractal operator. To achieve such modeling, a new method is introduced using Turlington polynomials to obtain continuous and differentiable functions. These functions are then fractal interpolated to yield fine structure. Spectral decomposition is used to obtain a differential equation model which is then fractal interpolated to forecast a fBm trajectory. This paper presents an overview of the theory and our modeling approach along with example results.

Graphics based intelligent search and abstracting using Data Modeling

This paper presents an autonomous text and context-mining algorithm that converts text documents into point clouds for visual search cues. This algorithm is applied to the task of data-mining a scriptural database comprised of the Old and New Testaments from the Bible and the Book of Mormon, Doctrine and Covenants, and the Pearl of Great Price. Results are generated which graphically show the scripture that represents the average concept of the database and the mining of the documents down to the verse level.

Muon imaging and data modeling

This paper describes our novel capability for analyzing voxel data such as muon images to detect potential threat objects and then to further discriminate them by shape. This is done with Change Detection using Data Modeling. These methods minimize long exposure requirements previously reported in the literature. This paper summarizes our algorithm and provides example results.

Data modeling of 1/f noise sets

A novel method is presented for solving the inverse fractal problem for 1/f noise sets. The performance of this method is compared with classical data modeling methods. Applicability to different distributions of noise is presented, along with an overview of important applications including data and image compression.

Shai-Hulud: The quest for worm sign

Successful worm detection at real-time OC-48 and OC-192 speed requires hardware to extract web based binary sequences at faster than these speeds, and software to process the incoming sequences to identify worms. Computer hardware advancement in the form of field programmable gate arrays (FPGAs) makes real-time extraction of these sequences possible. Lacking are mathematical algorithms for worm detection in the real time data sequence, and the ability to convert these algorithms into lookup tables (LUTs) that can be compiled into FPGAs. Data Modeling provides the theory and algorithms for an effective mathematical framework for real-time worm detection and conversion of algorithms into LUTs. Detection methods currently available such as pattern recognition algorithms are limited both by the amount of time to compare the current data sequence with a historical database of potential candidates, and by the inability to accurately classify information that was unseen in the training process. Data Modeling eliminates these limitations by training only on examples of nominal behavior. This results in a highly tuned and fast running equation model that is compiled in a FPGA as a LUT and used at real-time OC-48 and OC-192 speeds to detect worms and other anomalies. This paper provides an overview of our approach for generating these Data Change Models for detecting worms, and their subsequent conversion into LUTs. A proof of concept is given using binary data from a WEBDAV, SLAMMER packet, and RED PROBE attack, with BASIC source code for the detector and LUT provided.

Network-centric decision architecture for financial or 1/f data models

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

Real-time visual astronomy using image intensifiers and data modeling

This paper presents the use of an image intensifier as an astronomical eyepiece for real-time observation of deep sky objects. The optical frequency spectra of observational astronomical objects are reported and compared with the spectral response curve of a Generation 3 intensified optical system. Image intensifier performance on various classes of deep sky objects is also reported along with methods for performing real-time image enhancement.

Data modeling enabled dynamical analysis for blogger state-of-mind modeling and prediction

We present a novel mathematical framework for Data Mining blogger text entries and converting latent conceptual information into analytical predictive equations. These differential equations are conceptual models of the bloggers topic and state-of-mind transition dynamics. The mathematical framework is explored for its value in characterization of topic content and topic tracking as well as identification and prediction of topic dynamic changes.