stackconf 2021 | Data Driven Security

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©2021 Check Point Software Technologies Ltd.
[Protected] Distribution or modification is subject to approval
Peter Elmer | Security Expert, EMEA | Office of the CTO
May 2021
The value of Machine Learning
in Cyber Security
DATA DRIVEN SECURITY

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• Need for Data Driven Security
• Methods used
• Value of Machine Learning powered by human experience
• Effectivness of Data Driven Security
Today we look at …

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Collaboration
Intelligence
Experience
Key Ingredients For Success
Check Point Software Technologies
Founded in 1993, about 5.400 employees
Securing more than 100.000 customers
27 Years

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“Important decision
points are taken by
machines with logic
created from data.”
Check Point, Data Scientists Team
October 2020

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Predicting Results Using Machine Learning
Humans deciding on features and labels
oval round
smooth surface undulating surface
sweet sour
‘for pie’ ‘for vine’
Data remains
Data destroid
Human experience is key when
assigning characteristics (features)
for predicting a result (label)

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Predicting?

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Logic Created From Data
Computer Logic
Data
Program
Deterministic result
Humans deciding for the best logic to achieve a result prior to ‘feeding’ the machine
Context Assumptions Conceptions
Machine Learning Algorithm
Data
Result
Characteristics of data (features) of historic results (labels) are presented to machine
Program / Model
Logic
Program / Model
Logic
New Data Probablistic result

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Probabilistic results?

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Probabilistic
Deterministic

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Vectorising

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Feeding more data
into the machine
increases accuracy

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Limited resources
Increasing
attack surface

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Attacking Is Easier Than Defending
Surface
• Intent
• Idea
• Plan
• Design Logic
• Source Code
• Compile
• Stream of bits
Process
Effort for defending
Effort for
defending

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Understanding
Intent
Optimizing
Resources

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8 : 1
Applying Machine Learning requires
eight times less resources than preparing the data

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Mathematical
Representation
Abstraction

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• An image of 224x224 RGB
is transformed by filters
becoming a number
• Convolutional filters
capture 3x3 pixels to
capture notion of ...
• right/left
• up/down
• center
• Accuracy of 92,7%
Changing Representation
Turning an image into a number – VGG16 Convolutional Network
Source: Neurohive – VGG16 Convolutional Network for Classification and Detection:

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• Training a VGG16 with
fotos from Citiscapes
• Enhancing realismn of
animation
• Eliminating artefacts
Turning an image into a number – VGG16 Convolutional Network
Source: Intel - Enhancing Photorealism Enhancement, May 2021

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Vectorising Elements – Example: Human Language
Describing meaning / intent to achieve an abstraction level
King
Queen
Man
Woman
Masculinity Femininity
Vectorising words allows ‘word algebra’ - Algebra allows Machine Learning
swimming
swam
walking
walked
Verb tense
Vectors are presenting the abstraction level

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Vectorising Elements – Natural Language Processing (NLP)
“NLP is a subfield of computer science and artificial intelligence
concerned with interactions between computers and human (natural) languages.
It is used to apply machine learning algorithms to text and speech.”
Source: towards data science

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Vectorising Elements – Why is NLP useful?
Pineapples
We know ‘Pineapples are spikey and yellow’
are
spikey
and
yellow
Input Projection Output
‘Give me the missing word’
Pineapples
are
spikey
and
yellow
Input Projection Output
‘Give me the context’
Reference: Tomas Mikolov et al. : Distributed Representations of Words and Phrases and their Compositionality

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Understanding
what is making
something different
How can we apply this
to Cyber Security?

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Vectorising Elements – Cyber Security
Applying NLP when Sandboxing executables
Observing API calls performed against the operating system
API calls are language and can be vectorised

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Applying TF-IDF when disassembling OPCODES
Borrowing TF-IDF algorithm from word document analysis
Source: http://filotechnologia.blogspot.com/2014/01/a-simple-java-class-for-tfidf-scoring.html
“TF-IDF is an information retrieval and information extraction subtask which
aims to express the importance of a word to a document which is part of a
collection of documents which we usually name a corpus. ”

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Decoded machine language
Machine code has sequence – sequence has meaning

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• An executable file is fed into a neural network
• Each ‘filter‘ performs a mathematical operation
on a sliding patch
Turning an executable file into vectors – VGG16 Convolutional Network
Source: Check Point, Data Scientists Team, October 2020
Original Convolved

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Machine Learning In Cyber Security
Preventing Unknown Attacks
EXE
Understanding
Entropy & Structure
Disassembling
URL Verification
Finding Similarities
File/Registry
Classification
using provided
Meta Data
Verdict
Meta Data
PDF
PPT
DOC
XLS
PDF Analyzer
URL Verification
Macro Analyzer Classification
using provided
Meta Data
Verdict
Meta Data

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On July 20th 2020 a sample was labeled malicious by our machine learning logic

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On July 24th 2020 only 45 out of 73 engines on Virus Total labeled it malicious
Four days later!

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Sharing experience
Source: https://research.checkpoint.com/category/how-to-guides/

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‘Malware DNA’ based clustering applying TF-IDF
Two dimensional representation of
the 300 000 dimensional space
representing the ‘world of malware’
in Check Point Threat Intelligence
Colors representing labels of
malware families

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Itay Cohen (Check Point) and Omri Ben Bassat (Intezer) mapped out an ecosystem
Results:
• Classification into 60 families
and 200 modules
• 22 000 connections between
analyzed samples
• Different Actors don’t share code
Access the interactive map
• Published as open source
Download the detector tool
• Defend and contribute
Map based on Fruchterman-Reingold algorithm
Read the full report:
‘Malware DNA’ applied to uncover an APT Eco System

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Sharing experience
Understand how vulnerable on-premises and
cloud environments are
Source: https://research.checkpoint.com/2021/deep-into-the-sunburst-attack/
Understanding the SolarWinds Orion Platform Security Advisory
16-December 2020, video, https://community.checkpoint.com/

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The need for defense
BBC article about Colonial Pipeline attack, May 2021
Source: https://www.bbc.com/news/business-57050690
Source: Check Point, Research Blog, May 2021
Update 17th May 2021: DarkSide is offline - https://krebsonsecurity.com/

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Understanding
the DNA of a
malware allows
attributing ‘family’
characteristics

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Knowing the ‘family’
…allows applying
tools for defense
..allows saving
resources

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What‘s next?

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Machine Learning – General Purpose
Comparing NLP-Trained Models
Over 300 apps are using GPT-3
https://openai.com/blog/gpt-3-apps/
GPT-3 API access is controlled
https://openai.com/blog/openai-api/
28th May 2020
14 Apps using GPT-3

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Machine Learning Empowers Threat Prevention
Every input for Threat Intelligence becomes a Label
More than 27 years of experience …
• Having access to data
• Knowing the labels
• Selecting the right features
• Creating ML algorithms
• ML empowers Threat Prevention
Data
Labels This is
This is
Feature1: form
Feature2: colour
Next
module

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Machine Learning Empowers Threat Prevention
The infinity cycle of learning
Incumbent
New DATA
Labeling
Training
Stand by
evaluation
Decision point
Federated Learning
Using encrypted customer data
Supervised by human expertise
Measuring
Unseen data
Adjusting weights

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The infinity cycle of learning is powered by us

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Peter Elmer | Security Expert, EMEA | Office of the CTO
pelmer@checkpoint.com, May 2021
THANK YOU

stackconf 2021 | Data Driven Security

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stackconf 2021 | Data Driven Security