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National Institute of Standards and Technology, United States

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5 Papers

A Model Towards Using Evidence from Security Events for Network Attack Analysis

Authors: Changwei Liu ¹ , Anoop Singhal ² and Duminda Wijesekera ³

Affiliations: ¹ Geroge Mason University, United States , ² National Institute of Standards and Technology, United States , ³ George Mason University, United States

Digital Object Identifier: 10.5220/0004980300830095

Proceedings of WOSIS 2014/ICEIS, ISBN: 978-989-758-031-4

Quick Abstract

Constructing an efficient and accurate model from security events to determine an attack scenario for an enterprise network is challenging. In this paper, we discuss how to use evidence obtained from security events to construct an attack scenario and build an evidence graph. To achieve the accuracy and completeness of the evidence graph, we use Prolog inductive and abductive reasoning to correlate evidence by reasoning the causality, and use an anti-forensics database and a corresponding attack graph to find the missing evidence. In addition, because the constructed scenario and supplied evidence might need to stand up in the court of law, the federal rules of evidence are also taken into account to predetermine the admissibility of the evidence.

An Efficient Approach to Assessing the Risk of Zero-Day Vulnerabilities

Topics: Risk Assessment; Security Metrics and Measurement; Network Security

Authors: Massimiliano Albanese ¹ , Sushil Jajodia ² , Anoop Singhal ³ and Lingyu Wang ⁴

Affiliations: ¹ George Mason University, United States , ² George Mason University, The MITRE Corporation and, United States , ³ NIST, United States , ⁴ Concordia University, Canada

Digital Object Identifier: 10.5220/0004530602070218

Proceedings of SECRYPT 2013/ICETE, ISBN: 978-989-8565-73-0

Quick Abstract

Related Ontology Subjects/Areas/Topics (7)

Information and Systems Security	Risk Assessment	Security Metrics and Measurement
Information Assurance	Security in Information Systems	Wireless Network Security
Network Security

An Approach to Ontology-based Intention Recognition using State Representations

Topics: Knowledge Representation; Human-Machine Cooperation

Authors: Craig Schlenoff ¹ , Sebti Foufou ² and Stephen Balakirsky ³

Affiliations: ¹ National Institute of Standards and Technology and University of Burgundy, United States , ² University of Burgundy and Qatar University, France , ³ National Institute of Standards and Technology, United States

Digital Object Identifier: 10.5220/0004111701780183

Proceedings of KEOD 2012/IC3K, ISBN: 978-989-8565-30-3

Quick Abstract

In this paper, we present initial thoughts on an approach to ontology/logic-based intention recognition based on the recognition, representation, and ordering of states. This is different than traditional approaches to intention recognition, which use activity recognition and the ordering of activities. State recognition and representation offer numerous advantages, including the ability to infer the intention of multiple people working together and the fact that states are easier for a sensor system to recognize than actions. The focus of this work is on the domain of manufacturing assembly, with an emphasis on human/robot collaboration during the assembly process.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Knowledge Engineering and Ontology Development		Knowledge-Based Systems
Human-Machine Cooperation		Knowledge Representation		Symbolic Systems

A PUBLIC RANDOMNESS SERVICE

Topics: Security Information Systems Architecture and Design and Security Patterns; Identification, Authentication and Non-Repudiation; Security in Distributed Systems; Privacy; Trust Management and Reputation Systems

Authors: Michael J. Fischer ¹ , Michaela Iorga ² and René Peralta ²

Affiliations: ¹ Yale University, United States , ² National Institute of Standards and Technology, United States

Digital Object Identifier: 10.5220/0003612604340438

Proceedings of SECRYPT 2011/ICETE, ISBN: 978-989-8425-71-3

Quick Abstract

We argue that it is time to design, implement, and deploy a trusted public randomness server on the Internet. NIST plans to deploy a prototype during 2011. We discuss some of the engineering choices that have been made as well as some of the issues currently under discussion.

Related Ontology Subjects/Areas/Topics (8)

Data and Application Security and Privacy	Privacy	Security Information Systems Architecture and Design and Security Patterns
Identification, Authentication and Non-Repudiation	Security in Distributed Systems	Trust Management and Reputation Systems
Information and Systems Security	Security in Information Systems

THE LAGR PROJECT - Integrating Learning into the 4D/RCS Control Hierarchy

Topics: Vision, Recognition and Reconstruction; Mobile Robots and Autonomous Systems; Robot Design, Development and Control; Modeling, Simulation and Architectures

Authors: James Albus , Roger Bostelman , Tsai Hong , Tommy Chang , Will Shackleford and Michael Shneier

Affiliation: National Institute of Standards and Technology, United States

Digital Object Identifier: 10.5220/0001210701540161

Proceedings of ICINCO 2006, ISBN: 978-972-8865-60-3

Quick Abstract

The National Institute of Standards and Technology’s (NIST) Intelligent Systems Division (ISD) is a participant the Defense Advanced Research Project Agency (DARPA) LAGR (Learning Applied to Ground Robots) Project. The NIST team’s objective for the LAGR Project is to insert learning algorithms into the modules that make up the 4D/RCS (Four Dimensional/Real-Time Control System), the standard reference model architecture to which ISD has applied to many intelligent systems. This paper describes the 4D/RCS structure, its application to the LAGR project, and the learning and mobility control methods used by the NIST team’s vehicle.

Related Ontology Subjects/Areas/Topics (6)

Informatics in Control, Automation and Robotics		Modeling, Simulation and Architectures		Robotics and Automation
Mobile Robots and Autonomous Systems		Robot Design, Development and Control		Vision, Recognition and Reconstruction