Yin Pan

ON THE COMPUTATION OF UNCERTAINTY MEASURE IN DEMPSTER-SHAFER THEORY

An algorithm for computing the recently proposed measure of uncertainly AU for Dempster-Shafer theory is presented. The correctness of the algorithm is proven. The algorithm is illustrated by simple examples. Some implementation issues are also discussed.

BAYESIAN INFERENCE OF FUZZY PROBABILITIES

Abstract In this paper, we propose a new method to generalize Bayesian methods both for fuzzy likelihood and fuzzy prior probabilities. Based on interval Bayesian inference developed by Pan and Klir [1996], the proposed method overcomes the difficulty in developing a normalized fuzzy Bayesian inference recognized in the literature [Friihwirth-Schnatter, 1993].

Decentralized virtualization in systems administration education

The Networking and Systems Administration Laboratories are vital components of the applied academic experience of the students of the Networking, Security and Systems Administration Department. Students are currently able to implement multiple heterogeneous computing infrastructures using Symantecs Ghost Solution Suite. This paper presents and explores virtual lab environments that support alternative preparation solutions that will increase laboratory utilization, student productivity, and provide students with exposure to virtualization technology.

Forensic course development

In recent years, digital technology has experienced dramatic growth. Many of these advances have also provided malicious users with the ability to conceal their activities and destroy evidence of their actions. This has raised the need of developing specialists in computer digital forensics -- the preservation, identification, extraction and documentation of evidence stored in the form of digitally encoded information (data).In this paper, we present the procedures and rationale used in the development of forensic courses at both the undergraduate and the graduate levels. We also demonstrate our decision making process of selecting topics included in each course.

Bayesian inference based on fuzzy probabilities

We propose fuzzy Bayesian inference on the basis of interval Bayesian inference developed by Pan and Klir. The proposed method overcomes the difficulty in developing a normalized fuzzy Bayesian inference recognized in the literature.

Bayesian Inference Based on Interval-Valued Prior Distributions and Likelihoods

Although Bayesian inference has been successful in many applications, its serious limitation is the requirement that exact prior probabilities be available. It has increasingly been recognized that this requirement is often not realistic. To overcome this limitation of classical Bayesian inference, we investigate a generalized Bayesian inference, in which prior probabilities as well as likelihoods are interval-valued. Employing the tools of interval analysis and the theory of imprecise probabilities, we develop a method for exact calculation of interval-valued posterior probabilities for given interval-valued prior probabilities and precise or interval-valued likelihoods. This method is further generalized for fuzzy likelihood and fuzzy probabilities later. The classical Bayesian inference is a special case of our method.

Route robustness of a multi-meshed tree routing scheme for Internet MANETs

We propose a layer 2 routing and forwarding scheme called multi-meshed tree (MMT) routing for MANETs connected to Internet. The specifications of this scheme were drawn from unique features of Internet MANETs. Evaluation of the robustness of routes in this scheme is provided along with the route failure notification delays, which is an important performance parameter of this scheme. Where available we have substantiated our results with simulation data. The results clearly show the significance of the redundant routes offered by MMT

Forensic course development: one year later

In a paper presented in 2003, <i>Forensic Course Development</i>, the authors described the procedures and rationale used for the development of an undergraduate and a graduate computer and network forensic course at Rochester Institute of Technology (RIT). After the undergraduate course was taught in the spring quarter of 2003, the contents of the lecture and lab materials, outcomes of the course, and student feedback were reviewed. This feedback will help improve both the instructional materials and their delivery. This papers focus is on the development of the computer and network forensic course, as well as the changes made during the review process to improve the computer and network forensic course.

Automated malware detection using artifacts in forensic memory images

Malware is one of the greatest and most rapidly growing threats to the digital world. Traditional signature-based detection is no longer adequate to detect new variants and highly targeted malware. Furthermore, dynamic detection is often circumvented with anti-VM and/or anti-debugger techniques. Recently heuristic approaches have been explored to enhance detection accuracy while maintaining the generality of a model to detect unknown malware samples. In this paper, we investigate three feature types extracted from memory images - registry activity, imported libraries, and API function calls. After evaluating the importance of the different features, different machine learning techniques are implemented to compare performances of malware detection using the three feature types, respectively. The highest accuracy achieved was 96%, and was reached using a support vector machine model, fitted on data extracted from registry activity.

Game-based forensics course for first year students

This paper focuses on the design and development of a game-based forensics course. This course uses the game-based learning (GBL) approach that builds the game in a real computing environment that has direct access to actual forensics tools from a forensics machine and the evidence from a suspect machine. Interactive visualizations will be used to help students to understand the intangible and inaccessible abstract concepts such as deleted/hidden/encrypted/over-written digital evidence.