Upload File

cyber 2020 pitchdeck 091217 - eecis.udel.eduover 100k malware variants created every hour *bad...

  • Home
  • Documents
  • Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules
25
WWW.CYBER2020.COM

Upload: doannhan

Post on 10-Apr-2018

218 views

Category:

Documents


4 download

Report

TRANSCRIPT

Page 1: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 2: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Deep Learning Meets Cybersecurity

Investor DeckSeptember 2017

WWW.CYBER2020.COM

Page 3: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 4: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

The Leadership Team

Experience: Research and DevelopmentJohn Cavazos, Ph.D. (CEO and Founder)

Current Associate Professor at University of Delaware JP Morgan Faculty Fellow at Inst. For Financial Services Analytics

Experience: Execution StrategyJay Modi (COO)

Former CTO at Lockheed MartinSenior positions at The SI Organization, CSC, and NASA

Page 5: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 6: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

* Over 100K malware variants created every hour

* Bad actors have embraced automation

* Good actors still construct malware detection rules manually

The Problem

Page 7: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 8: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

State-of-the-Art versusGraph-Based Malware Characterization

50% improvement in accuracy using Graphs!

• Feature Vector Characterization• Byte Entropy Histograms• Summarized Code Structure

• Graph-Based Characterization• Static Code Graphs• Dynamic Behavioral Graphs

…

Graphs

Static Graphs Dynamic Graphs

State-of-the-Art

Byte Entropy Summarized Code

1 0 1 1

Page 9: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 10: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

The Solution: Deep Learning Applied to Cybersecurity

Deep Learning High-Performance Cloud Computing

Training Data Sets: Repository of

Billions of Malware

20 Years R&D

Page 11: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 12: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Cyber 20/20 is an all-software product consisting of:1) a deep learning neural network in the cloud

2) a network tap used to extract and examine all binary files entering an enterprise

3) a web based user interface

The Product

Page 13: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 14: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Great Start!

* Awarded $600K contract from Naval Research Laboratory.

* Awarded 1.25 Million compute hours on Department of Energy Titan supercomputer (fastest US supercomputer) to enhance our characterization and machine learning models.

* Applied for patent application No. 15256883, “Malware Analysis and Detection Using Graph-Based Characterization and Machine Learning”

Page 15: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 16: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Target Market Verticals

• Insurance and Financial Firms

• DoD, DHS, IC

• Critical infrastructure and healthcare

Page 17: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 18: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Competitive / CooperativeLandscape

Page 19: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Barriers To Entry For CompetitionGraph-based

Characterization+

Machine Learning

• Patent pending for richest characterization of malware: graph-based characterization

• Know-how second to none: 20 years of code characterization and machine learning

• World-class experts in machine/deep learning, reverse engineering, high-performance computing, and malware analysis

Page 20: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 21: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Pricing and Projected RevenueRecurring Revenue Focus

Novel Pricing SchemeCost of Service : 1¢ per File

Projected RevenueProjected Profitability in Y2

Years

Organization Size

Monthly# of Files

Monthly Cost

Very Large 10 Million $100,000

Large 1 Million $10,000

Medium 250K $2500

Small 100K $1000 $ 0 Mil$ 10 Mil$ 20 Mil$ 30 Mil$ 40 Mil$ 50 Mil$ 60 Mil

2017 2018 2019 2020 2021

Page 22: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 23: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Capital Request

• 24-month runway• Early exit potential• Uses of funds

• Develop platform & product features

• Build sales and marketing engine$X.X Mil

Page 24: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

WWW.CYBER2020.COM

Page 25: Cyber 2020 PitchDeck 091217 - eecis.udel.eduOver 100K malware variants created every hour *Bad actors have embraced automation *Good actors still construct malware detection rules

Deep Learning Meets Cybersecurity

John Cavazos, [email protected]

Jay Modi, [email protected]

WWW.CYBER2020.COM


Windows Malware Firewall: Remove Windows Malware Firewall

Intrusion Detection and Malware Analysis - Malware analysis · Intrusion Detection and Malware Analysis Malware analysis Pavel Laskov Wilhelm Schickard Institute for Computer Science

Malware Fails Best Bugs in Malware Felix Leder [Malware ... · 1 Malware Fails Best Bugs in Malware Felix Leder [Malware Detection Team] [email protected] 5. desember 2011 malware

Malware Analysis Analysis.pdf · Definisi Malware Analysis • What is a malware? • Malware (malicious software) ... Malware Sinkronisasi Token • Pelaku: Zeus Banking Trojan •

Reversing malware analysis training part8 malware memory forensics

An Introduction To Keyloggers, RATS And Malware€¦ · Malware Malware has been a problem for ages, Malware is short form of malicious software. A Malware is basically a program

Intrusion Detection and Malware Analysis - Malware collection

Evaluating Open Source Malware Sandboxes with Linux malware

Improving accuracy of malware detection by …...FFRI, Inc. Background – malware and its detection 4 Increasing malware Targeted Attack (Unknown malware) Malware generators Obfuscators

NET Hacking & In-Memory Malware...Adversary Emulation @ MITRE •Red teaming, but specific threat actors •Use open-source knowledge of their TTPs to emulate their behavior and operations

Android Malware Exposed-Evolution of Android Malware

Kuvempu University Librqarykuls-ir.kuvempu.ac.in/newspaper_clippings/pdfs/ns2017/dec17/kupcs-091217.pdf · Kuvempu University Library Jnana Sahyadri, Shankaraghatta Newspaper Clipping

Chapter 8 Malware - FTMS · – Boot virus – Logic Bomb virus – Directory virus – Resident virus. CSCA0101 Computing Basics 8 Malware Types of Malware ... Malware Types of Malware

091217 Lisbonian Magazine Jan 2010

Quantum of Surveillance - Electronic Frontier … of Surveillance: Familiar Actors and Possible False Flags in Syrian Malware Campaigns A joint Citizen Lab-EFF Report By Eva Galperin,

Malware and Anti-Malware Seminar by Benny Czarny

Malware Slums: Measurement and Analysis of Malware on ...homepage.cs.uiowa.edu/~mshafiq/files/malware... · Malware Slums: Measurement and Analysis of Malware on Traffic Exchanges

Intrusion Detection and Malware Analysis - Malware collection134.2.173.140/lehre/ws10/ids-malware/12-malware-collection.pdf · Intrusion Detection and Malware Analysis Malware collection

Malware Analysis Without Looking At Assembly Codegauss.ececs.uc.edu/Courses/c5155/pdf/malware-analysis.pdf · 2015. 11. 20. · Malware Analysis Malware typically employs encryption:

Malware Analysis and Antivirus Technologies: Kernel Malware & A Look at Malware … · 2011-08-16 · Malware Analysis and Antivirus Technologies: Kernel Malware & A Look at Malware

Traditional Actors and Other Actors

091217 Kuchins Northern Dist Net Web

“Setting the standard for protection, accuracy and easy integration” · 2019-09-18 · possible and malware actors seek to use all vectors at their disposal: exploits, virus,

ZGL PrintDNA Infographic 091217 - Zebra Technologies · Integrate with popular enterprise mobility management tools MDM/EMM Connectors Visibilty Services PRODUCTIVITY TOOLS Elevate

MALWARES MALWARE REVIEWED ISE DE Malware Reviewed · MALWARES There are public reports about spreading of malware named as ServHelper malware. It is a backdoor malware used by attacker

THE EVOLUTION OF MALWARE & FUTURE MALWARE MITIGATION

Part 4: Malware Functionality Chapter 11: Malware Behavior Chapter 12: Covert Malware Launching Chapter 13: Data Encoding Chapter 14: Malware-focused Network

THREAT COMPASS - Blueliv...Threat Compass Threat Context provides security teams with continuously updated and intuitive information around threat actors, campaigns, IOCs, malware,

RFP# 1805-091217 REQUEST FOR PROPOSALS FOR 1805 091217 HWD... · RFP #1805-091217 Airport Infrastructure Plan Aviation Consulting Services REQUEST FOR PROPOSAL (RFP) AIRPORT INFRASTRUCTURE

Metamorphic Malware 1 Metamorphic Malware Research

Threat Intelligence - IB-Bank · “The set of data collected, assessed and applied regarding security threats, threat actors, exploits, malware, vulnerabilities and compromise indicators.”

Hunting New Malware Actors - csee.umbc.eduFamous Exploit Kits • Black Hole is still the most famous, but there are many others: Angler, Sweet Orange, Redkit, Nuclear, RIG, Fiesta,

Mobile Malware Network View - Black Hat · Mobile Malware Network View ... ... Windows Malware impacts mobile

The power of cyber protection fileCyBrave Certified Malware Analysis Investigator (CCMAI|CCMAIE) Malware Analysis Investigator Entry Level Malware Analysis Investigator Malware Analysis

Introduction to Mobile Malware. Outline ➢ Introduction ➢ Types of Malware ➢ Malware examples ➢ How Malware Spreads ➢ Prevention ➢ AndroRAT Hands-on Lab