AI for PM.pptx

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©2022 Check Point Software Technologies Ltd.
[Restricted] ONLY for designated groups and individuals
April 2023
MACHINE LEARNING (ML)
FOUNDATIONS & GLOSSARY
Speaker Name | Speaker Title

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About me:
• In CP since Fall 21, after nearly 3 long months in Avanan
• M.Sc. from Weizmann in applied math (Non- linear dynamics)
• Prof. experience as a researcher, quant, and data scientist
Me & the web
• A (very small ) book in the Amazon store
• Two YouTube lectures
• Official author in “Towards Data Science”
• Two publications about ethics in AI
• Two publications in Arxiv
• A single publication in AAIML

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Data Becomes the Prom Queen
Until 2005-2010
• ML is an academic domain.
• Neural networks live in CSneuro-science departments -OCR
• Industry seldom sees data as a source of knowledge
Data is in town
• Internet
• Genome project – Data becomes a language
• A boost in the hedge funds market

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GLOSSARY

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Data Basic Terminolgy
Features – The data that we provide as an input
Target (Labels) - The values which the models predict
Examples
Features - Real estate DB ,Target – Housing Price
Features - Images, Target – {“Dog”,”Cat”,”Pineapple”:}
Feattures - email, Target {“Phishing”,” Spam”,”Benign”}
Features - IoT log , Target –{ IoT function }
Features - Medical DB, Target {“Lethal or not” /”Disease’s KPI”}

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TRAINING PROCESS

Early Steps
• Pre procssing & Features’ extraction- Aviad’s lecture
Data Split
Cyber caveat -(Phishing, FW rules)

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The Model & Its Training
Training is about optimizng the architecture’s weights

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UNSUPERVISED
&
SUPERVISED

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Unsupervised Learning
• There are features but no target
• Clustering -We search for patterns in the data.
• A coherent geometry of the data (explainable)

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Market Seg
Fraud

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Supervised Learning
• We have features - X
• We have a target - Y
Y determines the problem’s type:
Regression – Y is a continuos number
Classicafion – Y is a set of categories
Supervised learning is about prediction

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Regression – Use cases
• The price of a condo upon a real-estate DB Y-prices
• Risk – Y (money %)
• A product lifetime Y-time scaling

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Classification – Use cases
• Image object detection. Y ={Dog,Cat,Car}
• Sentiment analysis. . Y ={Sad, Happy,forlorn}
• Email phisihing Y ={Phishing,spam ,benign}
• Synthetic speech – Y = {Human,Recorded,Synth}
• Churn. Y={Yes, No}

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ML MODELS TRAINING

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Life vs. Junior High
The input X is not a single number but a type of data:
• Images
• A log of IoT device
• FW traffic (port,source,dest)
• SQL columns
No teacher, no function
In most of the problems Y is given
Supervised training – optimizing the map sfrom X to Y

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EVALUATION

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What does she mean?
Confusion matrix Common KPIs
• Accuracy =
𝑎+𝑑
𝑎+𝑏+𝑐+𝑑
. Correct samples
among the data
• Precision =
𝑎
𝑎+𝑐
. Correct samples among
detcted
• Detection=
𝑎
𝑎+𝑏
. Correct dtection among
malicious. ( Recall, 1-FR)
• FP=
𝐶
𝑐+𝑑
. Benign samples that detected
as malicios
“Malicious
”
“Benign”
Malicious a b
Benign c d

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WHY
DOLLAR IS
INFORMATIVE

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Churn Preidcion
“Churn” “Not”
Churn 990 10
not 1000 999000
Accuacy >99%
FP = 0.1%
Detection =99%
Is this a good model?
It depends on what the model misses
and the retainment costs

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Sytheic Speech
“Synth” “Human”
Synth 99 1
Human 5000 995000
Accuacy >99%
FP = 0.5%
Detection =99%
A single FP costs 5 USD – The machine costs 25000
USD for a single day

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Checkpoint Examples
• What is the cost of missing a phishing mail?
• What is the cost of false detection (on average) ?
IoT
An employee turns on the A/C with Lutron
An employee sends a file to a HP printer
An attack of a bot
Are all these potential errors equivalent?
Where does the term “Dollar” come from?

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A WORD ON EXPLAINABILITY

FINALLY
https://youtu.be/k3sTL5kCJ_4

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THANKS

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