Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

General framework for fault tolerance from ISO/ITU Reference Model for Open Distributed Processing (RM-ODP)

Abstract: The ISO Reference Model for Open Distributed Processing (RM-ODP) is a software engineering enabler for the software architecture of distributed systems. It addresses the non-functional properties of distributed systems in terms of a framework of concepts, structuring rules, semantics and mechanisms. One such property is fault tolerance (FT). This paper provides a view of how RM-ODP addresses the FT framework. RM-ODP defines a model of FT and different recovery mechanisms that include recovery of an object, a group of objects, an interface, a binding, a bound interface, etc. Mechanisms for FT include a distinguished FT control object for the management of FT, replication, checkpoint/recovery, relocation, migration, and several transparencies to hide the FT mechanisms from the application developer. System-defined policies and schemata drive the FT. A brief comparison of this with the emerging proposal for a fault-tolerant version of the Common Object Request Broker Architecture (CORBA) is also provided.

Big-Data Architecture for Cyber Attack Graphs Representing Security Relationships in NoSQL Graph Databases

Abstract: We introduce a new modeling framework for mapping vulnerability paths through networks and associating them with observed attacker activities. We merge a complex blend of network relationships and events, such as topology, firewall policies, host configurations, vulnerabilities, attack patterns, intrusion alerts, and logs. Our persistence layer includes Neo4j, a Not Only SQL (NoSQL) database optimized for graphs. We explore how the Neo4j property-graph data model supports analysis and queries within our problem domain. For interoperability with other tools, we employ standardized cyber- security data exchange language. We show that our approach supports the same kinds of graph analytics as an existing attack graph tool, through the application of the Neo4j Cypher query language. We then extend those analytics through a much richer model of the network environment and attacker/defender activities. Our work represents the first investigation of cyber attack graph analysis based on graph databases - an important class of NoSQL database.

An universal input and output RNS converter

Abstract: A systolic architecture is presented which is capable of both input and output conversion with a throughput equal to that of the fast residue number system (RNS) processes of addition and multiplication. The converter can be used with an arbitrary RNS (within certain realization-imposed limits). An actual anticipated VLSI layout is described that will be programmable for RNSs with up to eight moduli of six bits or less. This should provide an off-the-shelf solution for many RNS conversion requirements. >

Agent-directed simulation for systems engineering

Abstract: As the fielding of enterprise systems of systems becomes common it becomes increasingly important to understand the interactions between the systems as well as the important role that human behavior plays. This paper suggests that Agent-Directed Simulation is a valuable and crucial analysis tool for the Systems Engineer. The paper examines the concept of Agent-Directed Simulation for Systems Engineering and then introduces the notion of Human Complex Systems. An analysis infrastructure is described and a case study is provided to illustrate the concepts.

The autoregressive backpropagation algorithm

Abstract: Describes an extension to error backpropagation that allows the nodes in a neural network to encode state information in an autoregressive 'memory'. This neural model gives such networks the ability to learn to recognize sequences and context-sensitive patterns. Building upon the work of A. Wieland (1990) concerning nodes with a single feedback connection, the authors generalize the method to n feedback connections and address stability issues. The learning algorithm is derived, and a few applications are presented. >