This slide show how Machine Learning can be related to piece of security topics.

1. ENHANCE SECURITY
DOMAIN WITH
MACHINE LEARNING
M. Dzikry Ramdhani S.Kom M.MSI
Aries Fitriawan S.Komp M.Kom
Seminar Nasional Unmask Cyber Crime
Surabaya, 29 Oktober 2017

2. HELLO
WORLD !
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3. Overview
• What is Machine Learning?
• Machine Learning Technique
 Supervised vs Unsupervised Learning
 Dimensionality Reduction
 Feature Elimination
• Machine Learning Tools
• Machine Learning in Security
 Related Topic in Security
 Data Extraction for Network Security (include tools)
• Future Opportunity
 Deep Learning in Network Industry (Introduction)
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4. 1.
What is Machine
Learning?
- The journey begins -

5. “
Study about algorithm to
improve the Performance
(P) of some Tasks (T) from
the Experience (E).
- Mitchell, T. (1997)
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6. BIG CONCEPT
Learning = Improving with experience at some
task
Improve over task T,
with performance measure P,
based on experience E.
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EXAMPLE
T: Play chess
P: % of games won in world tournament
E: opportunity to play against self

7. Main Cases of Machine Learning
▪ Data mining: using historical data to improve
decisions
medical records -> medical knowledge
▪ Software applications we can't program by hand
autonomous driving
speech recognition
▪ Self customizing programs
finding user interests
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8. Too Difficult to Program
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9. Software that Customizes to User
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10. Machine Learning vs Data Mining
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Source : https://www.slideshare.net/LonghowLam/machine-learning-overview

11. 2.
Machine Learning
Technique
- Most Important Thing -

12. “
12
REAL OBJECT
FEATURE SPACE
OUTPUT
Machine
Learning

13. x f(x)
x = data input
f(x) = cluster function
x f(x) y
x = data input
y = output class

14. Supervised vs Unsupervised
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Supervised Learning Unsupervised Learning

15. 15
Supervised vs Unsupervised

16. 16
Supervised vs Unsupervised
Source : https://www.youtube.com/watch?v=xtp8AJ2Yd1E

17. 17
Supervised vs Unsupervised
Supervised Learning
Classification
(Categorial)
Regression
(Continous)
K-Nearest
Neighbor
Support Vector
Machine
Naïve Bayes
Bayesian Network
Linear Regression
Non-Linear
Regression
Support Vector
Regression
Neural Network, Deep Learning, Ensemble
Method
Unsupervised Learning
Clustering
Assossiation
Learning
K-Means
Hierarchical
Clustering
Gaussian Mixture
FP-Growth
Apriori
DBScan
Neural Network, Deep Learning, Ensemble
Method

18. 18

19. 19
Dimensionality Reduction and
Feature Elimination
▪Dimensionality Reduction
▫All original features are used
▫Transform feature into another data dimension
▫Method : PCA, SVD
▪Features Selection
▫Only a subset of the original features are used
▫Method : RFE (Recursive Feature Elimination)

20. 3.
Machine Learning
Tools
- Not Perfect but Handy-

21. Programming Language
21
Top 8
Programming
Languages For
Machine Learning
& Data Science:
1. Python.
2. Java.
3. R.
4. C++
5. C.
6. JavaScript.
7. Scala.
8. Julia.

22. Tools
Coding Simplified
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Theano, Caffee, Microsoft Azure (ML Studio), IBM Watson, KNIME

23. 4.
Machine Learning for
Internet Security
- The Main Course -

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25. Motivation
Fast
No Daily Human Repeat
CUT LONG PROCCESS
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26. DOMAIN / TASK
▪SPAM FILTERING
▪Network Defence
▪ATI (Advance Threat Intelegence)
▪Malicious Detection
▪NIDS (Network Intrusion Detection System)
▪ABC (Attacker Based On Correlation)
▪Etc
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27. Ex. SPAM Filtering
▫Purpose of SPAM
○ Advertisement
○ MLM
○ Politic Email
○ Stock Market
○ Etc.
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28. Spam Problem
▪ Fraud
▪ Consumes computing resources and time
▪ Reduces the effectiveness of legitimate advertising
▪ Cost shifting
▪ Identity Theft
▪ Consumer Perception
▪ Global Implication
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29. 29

30. Email Structures
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31. 5.
Future Oportunity
- The future is now -

32. 32
Implementation Challange
▸ lack of data: limited or no history of previous attacks
(required by supervised learning model).
▸ evolving attacks: attackers that constantly change
their behaviours, making current models obsolete.
▸ limited resources: costly and time consuming.

33. Introduction to Deep Learning
! deep learning isn’t branch of science, it’s a method.
! It began with Neural Networks, but “more deep”
Keyword search : Deep Belief Networks, Restricted Boltmann Machine, Deep Boltzmann Machine, Deep Convolutional
Networks, Deep Recurrent Networks
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34. Future Oportunity
▪Learn across full mixed-media data
▪Learn across multiple internal data, plus the web and feeds
▪Learn by active experimentation
▪Learn decisions rather than prediction (reinforcement
learning)
▪Cumulative, lifelong learning
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35. Learning Source
“Deep Learning” website
http://deeplearning.net/
Tensorflow and Keras
https://www.tensorflow.org/tutorials/
https://keras.io/
Siraj Raval’s Youtube Channel
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36. THANKS!
Any questions?
You can find us at:
m.dzikri.ramdhani@gmail.com
aries.f1991@gmail.com
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