Computer Science Collaboration Ideaspeople.cs.vt.edu/~ribbens/d/Hume/Hume and CS Oct 2017.pdf · cognitive jamming campaign Representative Programs SC2 Spectrum Collaboration Challenge

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Hume OverviewComputer Science Collaboration Ideas

Oct 2017

Dr. Joseph Mitola IIIDirector, Information Systems Laboratory

Aerospace Systems Laboratory

Electronic Systems Laboratory

0 Information Systems Laboratory

Office of Engagement and Learning

Hume Center Organization and Leadership

Hume as a $10M University-level Center 2

Office of the DirectorOffice of Finance and

Operations

Jon BlackDirector

Alan MichaelsDirector

Kevin HeaslipAssociate Director

Christine CallsenDirector

Kira GanttAssociate Director

Joe MitolaDirector

Charles ClancyDirector

Mark GoodwinDeputy Director

Bob McGwierChief Scientist

Christie ThompsonDirector

NationalCenters

NSF Security and Software Engineering Research Center

NSA/DHS Center for Academic Excellence in Cyber Defense Research

Intelligence Community Center for Academic Excellence

70Researchers, Staff, and Professors

350Students EngagedAnnually

$12MAnnual Program Expenditures

400Annual AcademicPublications

Education and Outreach


Research Laboratories and Program Thrusts



InformationSystemsLaboratory

AerospaceSystems

Laboratory

Autonomy and Orchestration

Signals Intelligence &Cyber Operations

Resilient C4ISR Missions

ExperientialLearning

Sources of Funding

Program Thrust Breakdown

SIGINT andCyber

Resilient C4ISR Missions

Experiential Learning

Autonomy and Orchestration

Autonomous control of EM battlespace to combat adversary UAS platforms

SOSI situational awareness in degraded or denied sensing environments

Cybersecurity in airworthiness, failure modes, criticality, and common criteria

Low-power GPU platforms for cognitive EW missions, leveraging SDR and deep learning

Aerospace Systems Laboratory


Representative Programs

SquadXCore Technologies &Experimentation

HallmarkTA1: Tools/Technology

NAVAIRAirworthiness Center

Code 32Low-SWAP EW Payloads

Satellite ground station and cubesat launches

Distributed, cross-layer cognitive management engineSOSI, proximity operations, and tracking

Robust and secure control of platforms and payloads



Security for wireless telematics and telemetry

Converged RF missions and payloadsRobust waveforms and software radio tools

Radar spectrum sharing, spectrum management

EM awareness and waveform agility through software radio and machine learning

Low-cost space-qualified software radio platform and waveform design tools

Rapid waveform prototyping, signal classification, and networking

Leveraging cyber observables in inform cognitive jamming campaign


SC2Spectrum Collaboration Challenge

RF MicrosatRisk Reduction

Army ASA(ALT)Rapid Capabilities Office

Code 32EW Countermeasures

Use SIGINT to forecast political crises, disease outbreaks, and terrorist/military activity

Developing synthetic data of adversary activity to support advanced analytics

Threat assessment of cyber vulnerabilities in aircraft telemetry systems

Leveraging code annotations to automatically deploy critical code to secure co-processors

Information Systems Laboratory



IARPAMercury

MAATA1: Synthetic Data

DHS S&TAircraft Cyber Initiative

Compiler & Middleware Security Tools

Security and privacy of IoT devices and services

Deep learning and reinforcement learningVirtualized, real-time, and embedded systems

Critical infrastructure and safety-critical systems

Part of a Larger Ecosystem at Virginia Tech


College ofEngineering

PamplinCollege ofBusiness

College ofScience

College ofLiberal Arts &

HumanSciences

School ofPublic and

InternationalAffairs

HumeCenter

Virginia TechApplied

ResearchCorporation

150 Professors andResearchers

600 Students EngagedAnnually

Current Portfolio

250 Professors andResearchers

2000 Students EngagedAnnually

Growth by 2023

University Investment, Program Development,

Philanthropy

Interdisciplinary approach that jointly considers

technology, policy, science, and business aspects of today’s greatest global

security challenges


Tam, Eli, Cameron Patterson, Peter Athanas

Global Security: Can We Trust Armed Robots?

University of Florida

Workshop on

Autonomy in Contested

Environments, Feb 2017

Can We Trust Home and Office Internet of Things (IoT)


Wireless Offers a Rich Domain for CS Collaboration

4/28/2017 (C) 2017 Virginia Tech 12

Require,Specify,Design,& Test

Biophysical Environment

Rain, Ice, SnowCongestedContested

Radio Physics

IoBT

YearsMinutes

Cognitive Radio

(c) 2017 Virginia Tech 13

Prof. Chip Maguire (1997): “You Can’t, Joe”

Upload, App

IP Block, *.GRC

Self

NOT OK

OKSandbox

RF Front EndRF/IF

Content

Self

Fault Management

[Machine Learning]

COTS VMs/CPUs Realize General Recursion


The Halting Problem

A Turing Machine T cannot

examine the state-machine M

and input I of a second Turing

machine to determine whether it

will halt on that input

T(M,I) (T and M are

partial, not total functions)

Static analysis of TE code is

incomplete at best (see Lang

Sec) for academic frustration

with the status quo

Th

e to

tal

fun

ctio

ns

(su

b-T

uri

ng)

A Radio cannot be Turing-equivalent (no (x)!)


C(tx,rx)

IF/ Baseband Digital Signal

Processing (DSP)RF/IF

ContentRF/IF RX Audio Speaker

RF/IF TX AudioContent Microphone

Control State Machine

Map 1:1, ONTO f-1

| 1.022 milliseconds for GSM |

J. Mitola III, “Software Radio Architecture: A Mathematical Perspective” IEEE JSAC May 99

Cognitive Radios (and Robots) OOPDAL Loop


• Reinforced Hierarchical

Sequences

• Case Based Reasoning

(CBR) vs Rule Bases

• Radio Knowledge

Representation

Language (RKRL)

Cognitive Radio Self Knowledge

© 2002 for the US DoD

User

Environment

Antenna RF Modem Baseband User Interface

Hardware

INFOSEC

Protocol Stack, ControlModem, Equalizer, etc.

Software Radio

Software Modules …..

KnownSelf

RKRL = Radio Knowledge Representation Language

Cognition CycleCognition

EnvironmentSpace-TimePropagationNetwork

Meta-Software

Antenna RF Modem

INFOSEC

BasebandUser Interface

Equalizer Model

Variable Bindings

...

User(s)IdentityNeedsBehaviors ...

RKRL FramesOntologiesDescriptorsEffectors

Adapted from Cognitive Radio, Doctoral Dissertation, KTH, Stockholm, Jun 2000 (www.it.kth.se/~jmitola)

Cognitive Dissonance of Autonomous Systems

• Cognitive dissonance theory (Festinger, 1957) [quoted in 1]• Widely adopted to explain various types of cognitive clash• Evolved into a theory capable of explaining a variety of behaviors and

outcomes in which people strive to maintain a consistent concept of self (Aronson, 1995).

• Root kits should generate “computational cognitive dissonance”• Turing Machines: the Halting problem renders f(g(x), g’(x)) unknowable• Register Machines: Cantor diagonalization on Monitor() functions

• A complete set of monitor functions requires the power set of the natural numbers• In 1839, Cantor proved for N (cardinality Aleph 0) {N}! Is not countable (hence, Aleph 1)• Mitola noted that Monitor({R}, Turing ISA) is Aleph 1 (Ligatti, Mitola, Landwher 2012 ) • Gödel proved in 1937 that [Turing Machines] are self-referentially inconsistent


Gödel Proof of the Inconsistency of Formal Logic


Gödel numbering expresses logic in the well-understood countable domain N (

Natural Numbers 1, 2, 3, …) with deductive logic specified as (premises =>

conclusions) he infers Turing-equivalent premises, and proves that “This conclusion

cannot be proven” nor can an uncountably infinite number of others (WFF)

Therefore, all general purpose computing is

self-referentially inconsistent

The Halting Problem, Partial Recursion

17

Advanced Persistent Threat (APT)

(c) 2017 Dr. Joseph Mitola III 20

Spear Phishing

Enterprise at Risk

(c) 2017 Dr. Joseph Mitola III 21

2nd Stage Implant

Deploys 2nd Stage Droppers

SOURFACE Downloader

Dropper Malware

Document With Exploit

Spearphishing Email

Obtains 2nd

StageC2

Server

Local copying defeats closed networksOne variant of CHOPSTICK focuses on apparent air

gap / closed network capabilities by routing messages between local directories, the registry

and USB drives.

The Results

Successfully Attacking “Isolated” Factory Floor Networks

Industrial Robots and Controls (IIoT)


Cyber-physical Attacks


Stuxnet 2007

Compliance is Not Good Enough


Let’s Call It “Comput-ications”


Compute

- ISA

- Registers

- OS

- Server

- App

- GUI

Communicate

- Network

- I/O

- Memory

- Hard drive

- Servers

- Protocols

- App 2 App

VPN, etc

Cybersecurity Is Impossible

4/28/2017 (C) 2017 Virginia Tech 26

Proof by Cantor Diagonalization (1839) on Monitor Functions (Ligatti 2010)

Power Set of {R}i is not decidable Monitor(Rj) can never be completed

{R}0 {R}2

i{R}1 {R}i

Registers (+ Memory (over Networks))

Mon(1)

Mon (2)

…

Mon (j)

Mon (j+1)

0 0 0 …. 1 1 1 0 …

0 1 0 …. 1 1 0 1 …

0 1 1 …. 1 1 0 0 …

Diagonal(Ex4(j, i=j)) = (0,1,1, … )

Not(Diagonal(Ex4(j, i=j))) = (1,0,0, … ) Mon*

Recursively Enumerable Sequence

Monit

ors

Proof by Cantor Diagonalization (1839)

Hackers [Erickson] Observe, Invert, Probe Until {R}j..k X (= hacked)

On COTS General Purpose CPU - von Neumann Memory Architectures

Dataflow Computing Overcomes Turing-von Neumann


General Recursive Total Recursive

Women, Fire and Dangerous Things

Machine DNA [6] (Self)


Shortfalls of Computational Linguistics

4/28/2017 (C) 2017 Virginia Tech 30

Learning Specifications


But VHDL is Too Hard


• Native VHDL builds a custom computing machine

• Hardware skills required

• Cross-compilers (Matlab etc.) are not efficient

Semantic Cross-compiler

4/28/2017 (C) 2017 Virginia Tech 33

OriginalMatlab C-code

For the Radio

Semantics

The Software Layers


CPU/RAM

Operating System (OS)

C, C++ Libraries, DLLs

App Languages: Java,

Java Virtual Machine (JVM)

Scripting Languages

Perl, Python, Ruby

Virtual Machine (VM)

SandBox

Protocols: HTTP

Browser: HTML, PHP, JavaScript

APT

APT

APT

APT

APT

$$$

6 Layers

106

Attack

Surfaces?

SCL Describing Human Domains

4/28/2017 (C) 2017 Virginia Tech 35

(Actors) are certain types of people and organizations, e.g. [having /skills\] such as a(First Responder [who can /put out a fire\]) (Fireman)

Deep Learning of Languages


System Security In The Hardware (SSITH)

37

HOPS SSITH Architecture

38

Horizontal Semantics-enhanced APIs

Memory-checking HOPS Pipes Oversee Dedicated Processing Unit (DPU) Code

Result

MEM

40G

MEM

Crypto Key

Bits

Virtual

Memory

MEM

SSD

PHY Layer

Payload

MEM

Packets

MEM

DPU 1

API

APIJSONData Py Thread Data

VTrackData C Thread Data

VV

PD

APIData JS Thread Data HTTP

Process Dispatch (PD)

Window

MEM

APIAPI

API

WireDisplay

MEM

API

Horizontal

Hadoop

Function Pipe

MEMORY BLOCK

VALIDATED USE

Language (Metaphor) in Software Engineering

• Metaphors are used to understand and experience one thing in terms of another [6, 7].

• Metaphor refers not only to instances in which both worlds of concepts correspond to one another, but also to cases in which they do not. If both worlds of concepts are identical, the metaphor is not a metaphor of that thing, but rather the thing itself.

• Beck [1] suggests 'driving' as a metaphor for software development. • Similar to car driving, software development is controlled by the execution of many small

adjustments. • Feedback is required when the driver is a slightly “off”• Many opportunities for corrections at a reasonable cost. • This metaphor, however, is limited: No teamwork is involved in driving. Still, this metaphor is

worthwhile.

• Several attempts to use metaphors in software projects • All reported difficulties in adopting this practice [2, 5, 8]. • Metaphor is a practice that requires a high level of cognitive awareness when implemented [3].


Scaling Up: Learning Algorithms from Examples


The Emerging Megatrend


Value Chain

Time

H A R

OS

Libraries

GUI, DB

Script E

CPU

App-Enabling Chips

Soft

war

e D

efin

ed R

adio

(S

DR

)S

oft W

ired R

ad

io (S

WR

)

Collaboration Ideas – joint pursuits

• DARPA• Domain-specific System on Chip (Tom Rondeau)

• Big Data in a Computing Domain (e.g. video games)

• Ontology and Computing Primitives

• NSF MURI• Reducing barriers to cyber security via self-aware sysems

• Ingest specifications to create rich computational models of the domain

• Specialize the models for various “self” subdomains (e.g. IIoT, self-driving cars)

• ONR• De-bloat, deconstruct, simplify code, reduce attack surfaces without changing function

• Specifications Design (SW, HW) Implementation Optimization


Academic Collaboration Ideas

• Applied Theory of Computing• Goedel, Turing, LangSec, Chip-sets, Integrated HW-SW Design, New Langauges, Tools

• Software-Defined Systems Engineering• Networks of Radios and Robots

• Cognitive systems = autonomous + trustworthy

• Other?


Documents

Computer Science Collaboration Ideaspeople.cs.vt.edu/~ribbens/d/Hume/Hume and CS Oct 2017.pdf · cognitive jamming campaign Representative Programs SC2 Spectrum Collaboration Challenge