Monoidal Computers, Networks and Strategic Learning: Methods for Adaptive Defense in Cyber Security (MADCybS)

reportActive / Technical Report | Accession Number: AD1107067 | Open PDF

Abstract:

We mapped the conceptual problem area and identified technical toolkits for the solutions. We made significant advances in a few technical tasks and small advances in several others. We defined the conceptual framework for recognizing and analyzing deception attacks and influence campaigns. An apparent paradigm shift in concept analysis emerged.

Author(s):
Pavlovic, Dusko
Author Organization(s):
UNIVERSITY OF HAWAII SYSTEMS HONOLULU HONOLULU United States
Descriptive Note:
Technical Report,15 Sep 2015,29 Feb 2020
Pagination:
0010

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release
Distribution Statement:
Approved For Public Release;

RECORD

Collection: TR
Identifying Numbers
Report Number(s):
AFRL-AFOSR-VA-TR-2020-0130
Monitor Series:
AFRL-AFOSR-VA-TR-2020-0130
 Subject Terms
Joint Capability Areas:
JCA_6_Net Centric; JCA_6.4.1_Secure Information Exchange; JCA_6.4_Information Assurance; JCA_1.2.6_Concepts; JCA_5_Command and Control; JCA_1.2_Force Preparation; JCA_1_Force Support; JCA_3.2_Engagement; JCA_3_Force Application; JCA_6.1_Information Transport; JCA_3.2.2_Non-Kinetic Means
Modernization Areas:
Cyber
Communities of Interest:
Cyber
Descriptor(s):
toolkits, moving target defense, CYBERSECURITY, reasoning, STRATEGIC ANALYSIS, decision making, GAME THEORY, learning, WEB SERVICE, internet
Field(s)/Group(s):
Computer Systems Management and Standards, Operations Research
Keyword(s):
Moving Target Defense, Strategic Learning, Computability, Game Model, Monoidal Networks, madcybs(methods for adaptive defense in cyber security)
Report Date:
2020 Aug 12
Creation Date:
2020 Aug 27