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Information Technology

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Displaying 26 - 50 of 4378

Proxima: A Proxy Model-Based Approach to Influence Analysis

September 25, 2024
Author(s)
Sunny Shree, Yu Lei, Raghu Kacker, David Kuhn
Machine learning (ML)-based Artificial Intelligence (AI) systems rely on training data to perform optimally, but the internal workings of how ML models learn from and use this data are often a black- box. Influence analysis provides valuable insights into

Assessing the Degree of Feature Interactions that Determine a Model Prediction

September 17, 2024
Author(s)
Krishna Khadka, Sunny Shree, Yu Lei, Raghu Kacker, David Kuhn
Machine Learning (ML) models rely on capturing important feature interactions to generate predictions. This study is focused on validating the hypothesis that model predictions often depend on interactions involving only a few features. This hypothesis is

On Combinatorial Security Testing for the Tor Anonymity Network Client

September 17, 2024
Author(s)
Dimitris Simos, Bernhard Garn, Dominik-Philip Schreiber, Manuel Leithner, David Kuhn, Raghu Kacker
In this paper, we present an application of combinatorial security testing to the well-known anonymity network Tor. Rigorous testing of the Tor network is important to evaluate not only its functionality, but also the security it provides to its users

Building a Cybersecurity and Privacy Learning Program

September 12, 2024
Author(s)
Marian Merritt, SUSAN HANSCHE, BRENDA ELLIS, Julie Nethery Snyder, KEVIN SANCHEZ-CHERRY, DONALD WALDEN
This publication provides guidance for federal agencies and organizations to develop and manage a life cycle approach to building a Cybersecurity and Privacy Learning Program (CPLP). The approach is intended to address the needs of large and small

Report on the Block Cipher Modes of Operation in the NIST SP 800-38 Series

September 10, 2024
Author(s)
Nicky Mouha, Morris J. Dworkin
This report focuses on the NIST-recommended block cipher modes of operation specified in NIST Special Publications (SP) 800-38A through 800-38F. The goal is to provide a concise survey of relevant research results about the algorithms and their

NIST Cloud Computing Forensic Reference Architecture

July 30, 2024
Author(s)
Martin Herman, Michaela Iorga, Ahsen Michael Salim, Robert H. Jackson, Mark R. Hurst, Ross A. Leo, Anand Kumar Mishra, Nancy M. Landreville, Yien Wang
This document summarizes the research performed by the NIST Cloud Computing Forensic Science Working Group and presents the NIST Cloud Computing Forensic Reference Architecture (CC FRA or FRA), whose goal is to provide support for a cloud system's forensic

Energy Efficiency Scaling for 2 Decades (EES2) Roadmap for Computing

July 14, 2024
Author(s)
Jim Booth
Abstract—In response to the looming crisis in global energy consumption required for advanced computing applications, the United States Department of Energy (DOE) Advanced Materials and Manufacturing Technology Office (AMMTO) is leading a national effort

A Review of the Literature on Voter Verification and Ballot Review

July 8, 2024
Author(s)
Whitney Quesenbery, Suzanne Chapman, Christopher Patten, Roberto Spreggiaro, Shanee Dawkins
One of the major issues for voting systems today is whether they provide voters with a meaningful opportunity to verify their ballot before casting it. This opportunity is important in helping them vote their intent by catching errors or omissions made

How Voters Review and Verify Ballots

July 8, 2024
Author(s)
Suzanne Chapman, Lynn Baumeister, Whitney Quesenbery, Shanee Dawkins
Qualitative research to gain deeper insights about how voters mark, review, verify, and cast their ballots. Conducted as part of the work to update the human factors—accessibility, privacy, and usability—requirements in federal voting system standards and

An Adaptable AI Assistant for Network Management

July 3, 2024
Author(s)
Amar Abane, Abdella Battou, Mheni Merzouki
This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The

Enhancing Network Data Plane Analysis with Native Graph Database

July 3, 2024
Author(s)
Amar Abane, Abdella Battou, Mheni Merzouki
As modern networks grow in complexity, ensuring their reliability and security becomes increasingly vital. Data plane analysis is a key process for verifying network behavior, but traditional data plane analysis tools face challenges in extensibility

Global Community Technology Challenge (GCTC) Strategic Plan 2024-2026

June 28, 2024
Author(s)
Michael Dunaway, Thomas Roth, Edward Griffor, David A. Wollman
This document provides a strategy and a project plan for the Global Community Technology Challenge, a federal smart cities program led by the Smart Connected Systems Division at the National Institute of Standards and Technology, an agency of the U.S

Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle

June 27, 2024
Author(s)
Jaganmohan Chandrasekaran, erin lanus, tyler cody, laura freeman, Raghu N. Kacker, M S Raunak, D. Richard Kuhn
The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to

Evaluating Large Language Models for Real World Vulnerability Repair in C/C++ Code

June 19, 2024
Author(s)
Lan Zhang, Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu
The advent of Large Language Models (LLMs) has enabled advancement in automated code generation, translation, and summarization. Despite their promise, evaluating the use of LLMs in repairing real-world code vulnerabilities remains underexplored. In this

Anomaly Based Intrusion Detection using Large Language Models

June 15, 2024
Author(s)
Zineb Maasaoui, Abdella Battou, Mheni Merzouki, Ahmed LBATH
In the context of modern networks where cyber-attacks are increasingly complex and frequent, traditional Intrusion Detection Systems (IDS) often struggle to manage the vast volume of data and fail to detect novel attacks. Leveraging Artificial Intelligence