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Machine Learning in Cyber Security
by : Vishwas Narayan
DISCLAIMER
I am not a Hacker so don't hack me.
●Theoretical Nature of
○ Definitions
○ Importance of these Technologies
○ Where to Find More resources
●Hands On Materials
○ Lab Setup(we will discuss the softwares that you need)
○ Basic Demonstration
●Guidance to Projects
○ Market Demand for Technologies
○ Project IDEAS
Topics Covered/Key takeaways
●JOBS
- Live online Openings(I will say some in the
part 2)
Motivation
●A working Docker IMAGE
○ With Python Library installed
○ Saved as LOCAL DOCKER IMAGE
○ Also we will push the image and pushed to the docker hub.
○ Seems Very simple but need to understand MANY Concepts such as
■ Virtualisation
■ Containers
■ Devops
(Will share a Lot of self study LAB books if you are motivated thus come reach me as soon as
possible)
Final OUTPUT (DEMO)
●Machine Learning (ML)
Machine learning is a data processing technique that automates the development
of analytical models. It is a subfield of artificial intelligence that is focused on the
premise that systems can learn from data, recognise patterns, and make decisions
with little to no human involvement.
Definitions
Definitions (Contd.)
●Cyber Security
Computer defence, also known as cybersecurity or information technology
security, is the safeguarding of computer systems and networks from information
leakage, theft or harm to their devices, applications, or electronic records, as well
as interruption or misdirection of the services they offer.
Definitions (Contd.)
●ML + Data Analytics + Cyber Security
Machine learning has been quickly adopted in cybersecurity for its potential to
automate the detection and prevention of attacks, particularly for next-generation
antivirus (NGAV) products. ML models in NGAV have fundamental advantages
compared to traditional AV, including the higher likelihood of identifying novel,
zero-day attacks and targeted malware, an increased difficulty of evasion, and
continued efficacy during prolonged offline periods
Definitions (Finally.)
Importance of these Technology
Technology is important because it makes you feel more secure with every area in life for both
personal and business reasons. With technology advancing more people are able to have
access to supplies such as fresh water and food because technology can help deliver those
items to people that otherwise couldn’t get it.
Where to find the resources
Google Hacking and Lib Genesis is the best way to get your resources as the
hackers.
●Virtualisation
○Type 1
■ ESXi,KVM
○Type 2
■ Vmware Workstation,VirtualBOX(hypervisors)
●Containers
○DOCKER
○LXC
○KUBERNETES
Implementation Technologies through
Implementation Technologies through (explained)
Devops implementation
DEVOPS LIFECYCLE
DEVOPS – Technology Mapping
●Docker is a ‘container technology’
○Linux-specific
■ can’t run Mac OSX, Windows in docker containers
■ But can run docker containers on Mac OSX & Windows
○Shrink-wrap your software, run it on any Linux platform
●Not a virtual machine
○Similar to virtual machines, but more lightweight
■ Smaller, faster to start, easier to maintain and manage
■ Lighter on system resources => vastly more scalable
○VM-thinking will lead to poor results, avoid it!
What is Docker? (the tech of the modern world and a
must know skill)
●Portability:
○No need to rebuild your application for a new platform!
■ Build a container once, run it anywhere
● Cori/Edison/Genepool/reliable on any(runs on any os)
● AWS/GCP/Azure
■ Stable s/w versions across all platforms, no runtime glitches for the testing and the
development people also
○Think of it as ‘modules-to-go’
■ Instead of ‘module load <container images>’ you ‘docker pull <container images>’
■ No waiting for modules to be built/deployed for you on your local pc just run and go dude.
●Reproducibility:
○Because your s/w is stable, your pipeline is reproducible
■ Run the exact same binaries again 10 years from now (if you know how it works)
Why use Docker?
●Computational workloads
○Use applications without having to install them
○Run your applications anywhere; clouds
○Reproducible pipelines – today’s focus
●Services
○Web portals/gateways (R/Shiny, Apache, Jupyter…)
○Persistent workflow manager interfaces (Fireworks…)
○Continuous build systems (Gitlab…)
○For prototyping or for production running (databases etc)
○All those things you run in the background on the login nodes today!
What can you do with it?
Docker Hub: Build, Ship, Run Applications
Build Ship
Run
Dev
QA
Source
Staging
Physical
Virtual
Cloud
Infrastructure Management
Infrastructure Management
DockerFile
Source Code
Repository
TEST
TEST
TEST
TEST
TEST
GCE RAX Azure
Mac/Win Dev
Machine
Boot2Docker
Docker
Analytics
DB
Prod
Machine
Linux OS
Docker
Docker
++
Users Collab
Provenance Policy
Docker Hub
Registries
Public Curated Private
Docker Hub API
Third Party Tools
Prod
Machine
Linux OS
Docker
Docker
Prod
Machine
Linux OS
Docker
Docker
VM
Docker
Docker
VM
Docker
Docker
VM
Docker
Docker
QA Machine
Linux OS
Docker
Docker
Docker Hub provides a centralized resource for container image discovery,
distribution and change management, user and team collaboration, and workflow
automation
●A recipe for building a container
●Start with a base image, add software layer by layer
○Choosing the base image has a big effect on how large your container will be: go small (‘alpine’ or
‘busybox’)!
●Add metadata describing the container
○Always a good idea
●Set the command to run when starting the container, map network ports, set
environment variables
○Not strictly needed for batch applications, useful for services (web apps, databases…)
Building a container: the Dockerfile
FROM debian:<any version you want>
# LABEL lets you specify metadata, visible with 'docker inspect'
LABEL Maintainer="Tony Wildish, wildish@lbl.gov" Version=1.0
# I can set environment variables
ENV PATH /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
# Commands to prepare the container
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update -y
RUN apt-get install --assume-yes apt-utils
RUN apt-get install -y python3
RUN apt-get install -y python3-pip
RUN apt-get clean all
RUN pip install bottle
# Add local files
ADD hello.py /tmp/
# open a port
EXPOSE 5000
# specify the default command to run
CMD ["python", "/tmp/hello.py“]
Dockerfile
Actual lab setup
From Example of the what can you do
with the docker file settings and lab
setup
Apache tomcat
Apache Tomcat is a Java-based Web server. It is important in this context because it
forms the basis for a number of key platforms, including DSpace, Fedora, and Islandora.
Apache Airflow
Apache Airflow is an open-source workflow management platform.
It started at Airbnb in October 2014 as a solution to manage the
company's increasingly complex workflows. Creating Airflow
allowed Airbnb to programmatically author and schedule their
workflows and monitor them via the built-in Airflow user interface.
DSpace
DSpace is an open source repository software package typically used for creating open
access repositories for scholarly and/or published digital content. Its design is focused on the
long-term storage, access and preservation of digital content.
File Information Tool Set
Anaconda Navigator(A go to Setup)
Web Min
Webmin is a Web-based system
configuration tool for Unix-like
systems, although recent versions
can also be installed and run on
Windows. Using any Web browser
that supports tables and forms (and
Java for the `File Manager` module),
Webmin enables a user to
administer a Linux or Unix system,
e.g., setup user accounts, Apache,
DNS, file sharing, etc., through a
graphical user interface.
nmap
Nmap is a free and open-source
network scanner created by Gordon
Lyon. Nmap is used to discover hosts
and services on a computer network by
sending packets and analyzing the
responses. Nmap provides a number of
features for probing computer
networks, including host discovery and
service and operating system
detection.
metasploit The Metasploit Project is a
computer security project
that provides information
about security
vulnerabilities and aids in
penetration testing and
IDS signature
development. It is owned
by Boston, Massachusetts-
based security company
Rapid7.
proxychains
datasets - What are "proxy data sets" in machine learning? - Artificial Intelligence Stack Exchange
DDOS Attack
Steganography
Shoulder surfing
Obfuscation
What can it be in real world?
Before that let me give you a quick introduction to the
hacking
1. CND and CEH certification.
2. CCNA Certification.
3. OSCP Certification.
4. OFCP Certification.
5. CompTIA pentest
There are lot of ways in which we can think about
1. Brute force attack
2. Dictionary attack
3. Hashing
4. Alternate data stream
●What is machine learning?
●Learning system model
●Training and testing
●Performance
●Algorithms
●Machine learning structure
●Learning techniques
●Applications
An Overview of Machine Learning
●A branch of artificial intelligence, concerned with the design and development of
algorithms that allow computers to evolve behaviors based on empirical data.
●As intelligence requires knowledge, it is necessary for the computers to acquire
knowledge.
What is machine learning?
Learning system model
Input
Samples
Learning
Method
System
Training
Testing
Something new every time
● Training is the process of making the system able to learn.
● No free lunch rule:
○ Training set and testing set come from the same distribution
○ Need to make some assumptions or bias
Training and testing
●There are several factors affecting the performance:
○ Types of training provided
○ The form and extent of any initial background knowledge
○ The type of feedback provided
○ The learning algorithms used
●Two important factors:
○ Modeling
○ Optimization
Performance
●The success of machine learning system also depends on the algorithms.
●The algorithms control the search to find and build the knowledge structures.
●The learning algorithms should extract useful information from training
examples.
Algorithms
●Supervised learning
○ Prediction
○ Classification (discrete labels), Regression (real values)
●Unsupervised learning
○ Clustering
○ Probability distribution estimation
○ Finding association (in features)
○ Dimension reduction
●Semi-supervised learning
●Reinforcement learning
○ Decision making (robot, chess machine)
Algorithms
●Supervised learning
Machine learning structure
●Supervised learning
Machine learning structure
●Unsupervised learning
Machine learning structure
●Unsupervised learning
Machine learning structure
●SPAM detection
○ Distinguish between SPAM and HAM emails
○ number of emails correctly classified
○ Hand-labeled emails
●Detecting catalog duplicates
○ Distinguish between duplicate and non-duplicate catalog entries
○ False positive/negative rate based on business criteria
○ Hand-labeled duplicates and non-duplicates
●Go learner
○ Playing Go
○ number of games won in tournament
○ Practice games against itself
Some Examples
●So many tools
○Preprocessing, analysis, statistics, machine learning, natural language processing, network
analysis, visualization, scalability
●Community support
●“Easy” language to learn
●Both a scripting and production-ready language
Programming Language - Why python?
A very complete list can be found at PyPi the Python Package Index:
https://pypi.python.org/pypi
To install, use pip, which comes with Python:
pip install package
or download, unzip, and run the installer directly from the directory:
python setup.py install
External libraries
●Data analysis and modeling
●Similar to R and Excel, Keep everything in Python
●Easy-to-use data structures
○DataFrame
●Data wrangling tools
○Merging, pivoting, etc
●Use for preprocessing
○File I/0, cleaning, manipulation, etc
●Combinable with other modules
○NumPy, SciPy, statsmodel, matplotlib
Pandas
and there are some more easy libraries
go to my repository here.
vishwas 1234567/ML-On-Azure (github.com)
●Machine learning module
●Open-source
●Built-in datasets
●Good resources for learning
●Very comprehensive of machine learning algorithms
●Preprocessing tools
●Methods for testing the accuracy of your model
Scikit-learn
●Natural Language ToolKit
●Access to over 50 corpora
○Corpus: body of text
●NLP tools
○Stemming, tokenizing, etc
●Resources for learning
○Lemmatizing, tokenization, tagging, parse trees
○Classification
○Chunking
○Sentence structure
nltk
https://www.crummy.com/software/BeautifulSoup/
Web analysis.
Need other packages to actually download pages like the library requests.
http://docs.python-requests.org/en/master/
BeautifulSoup navigates the Document Object Model:
http://www.w3schools.com/
Not a library, but a nice intro to web programming with Python.
https://wiki.python.org/moin/WebProgramming
Beautiful Soup
Just do one command
pip install zope
important resources
My channel has podcasts thus do see my podcasts on youtube and also some
community tutorials that i will be taking.
Vishwas Narayan -
YouTube
For other resources stay tuned for the series
Mark the playlist that is shared in the mail and also get the things moved in as the
things are going to be
Questions
Is everyone Still Alive and seeing me online???????

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Machine learning in cybersecutiry

  • 1. Machine Learning in Cyber Security by : Vishwas Narayan
  • 2. DISCLAIMER I am not a Hacker so don't hack me.
  • 3. ●Theoretical Nature of ○ Definitions ○ Importance of these Technologies ○ Where to Find More resources ●Hands On Materials ○ Lab Setup(we will discuss the softwares that you need) ○ Basic Demonstration ●Guidance to Projects ○ Market Demand for Technologies ○ Project IDEAS Topics Covered/Key takeaways
  • 4. ●JOBS - Live online Openings(I will say some in the part 2) Motivation
  • 5. ●A working Docker IMAGE ○ With Python Library installed ○ Saved as LOCAL DOCKER IMAGE ○ Also we will push the image and pushed to the docker hub. ○ Seems Very simple but need to understand MANY Concepts such as ■ Virtualisation ■ Containers ■ Devops (Will share a Lot of self study LAB books if you are motivated thus come reach me as soon as possible) Final OUTPUT (DEMO)
  • 6. ●Machine Learning (ML) Machine learning is a data processing technique that automates the development of analytical models. It is a subfield of artificial intelligence that is focused on the premise that systems can learn from data, recognise patterns, and make decisions with little to no human involvement. Definitions
  • 8. ●Cyber Security Computer defence, also known as cybersecurity or information technology security, is the safeguarding of computer systems and networks from information leakage, theft or harm to their devices, applications, or electronic records, as well as interruption or misdirection of the services they offer. Definitions (Contd.)
  • 9. ●ML + Data Analytics + Cyber Security Machine learning has been quickly adopted in cybersecurity for its potential to automate the detection and prevention of attacks, particularly for next-generation antivirus (NGAV) products. ML models in NGAV have fundamental advantages compared to traditional AV, including the higher likelihood of identifying novel, zero-day attacks and targeted malware, an increased difficulty of evasion, and continued efficacy during prolonged offline periods Definitions (Finally.)
  • 10. Importance of these Technology Technology is important because it makes you feel more secure with every area in life for both personal and business reasons. With technology advancing more people are able to have access to supplies such as fresh water and food because technology can help deliver those items to people that otherwise couldn’t get it.
  • 11. Where to find the resources Google Hacking and Lib Genesis is the best way to get your resources as the hackers.
  • 12. ●Virtualisation ○Type 1 ■ ESXi,KVM ○Type 2 ■ Vmware Workstation,VirtualBOX(hypervisors) ●Containers ○DOCKER ○LXC ○KUBERNETES Implementation Technologies through
  • 13.
  • 14.
  • 17.
  • 20. ●Docker is a ‘container technology’ ○Linux-specific ■ can’t run Mac OSX, Windows in docker containers ■ But can run docker containers on Mac OSX & Windows ○Shrink-wrap your software, run it on any Linux platform ●Not a virtual machine ○Similar to virtual machines, but more lightweight ■ Smaller, faster to start, easier to maintain and manage ■ Lighter on system resources => vastly more scalable ○VM-thinking will lead to poor results, avoid it! What is Docker? (the tech of the modern world and a must know skill)
  • 21.
  • 22.
  • 23. ●Portability: ○No need to rebuild your application for a new platform! ■ Build a container once, run it anywhere ● Cori/Edison/Genepool/reliable on any(runs on any os) ● AWS/GCP/Azure ■ Stable s/w versions across all platforms, no runtime glitches for the testing and the development people also ○Think of it as ‘modules-to-go’ ■ Instead of ‘module load <container images>’ you ‘docker pull <container images>’ ■ No waiting for modules to be built/deployed for you on your local pc just run and go dude. ●Reproducibility: ○Because your s/w is stable, your pipeline is reproducible ■ Run the exact same binaries again 10 years from now (if you know how it works) Why use Docker?
  • 24. ●Computational workloads ○Use applications without having to install them ○Run your applications anywhere; clouds ○Reproducible pipelines – today’s focus ●Services ○Web portals/gateways (R/Shiny, Apache, Jupyter…) ○Persistent workflow manager interfaces (Fireworks…) ○Continuous build systems (Gitlab…) ○For prototyping or for production running (databases etc) ○All those things you run in the background on the login nodes today! What can you do with it?
  • 25. Docker Hub: Build, Ship, Run Applications Build Ship Run Dev QA Source Staging Physical Virtual Cloud Infrastructure Management Infrastructure Management DockerFile Source Code Repository TEST TEST TEST TEST TEST GCE RAX Azure Mac/Win Dev Machine Boot2Docker Docker Analytics DB Prod Machine Linux OS Docker Docker ++ Users Collab Provenance Policy Docker Hub Registries Public Curated Private Docker Hub API Third Party Tools Prod Machine Linux OS Docker Docker Prod Machine Linux OS Docker Docker VM Docker Docker VM Docker Docker VM Docker Docker QA Machine Linux OS Docker Docker Docker Hub provides a centralized resource for container image discovery, distribution and change management, user and team collaboration, and workflow automation
  • 26. ●A recipe for building a container ●Start with a base image, add software layer by layer ○Choosing the base image has a big effect on how large your container will be: go small (‘alpine’ or ‘busybox’)! ●Add metadata describing the container ○Always a good idea ●Set the command to run when starting the container, map network ports, set environment variables ○Not strictly needed for batch applications, useful for services (web apps, databases…) Building a container: the Dockerfile
  • 27. FROM debian:<any version you want> # LABEL lets you specify metadata, visible with 'docker inspect' LABEL Maintainer="Tony Wildish, wildish@lbl.gov" Version=1.0 # I can set environment variables ENV PATH /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin # Commands to prepare the container ENV DEBIAN_FRONTEND=noninteractive RUN apt-get update -y RUN apt-get install --assume-yes apt-utils RUN apt-get install -y python3 RUN apt-get install -y python3-pip RUN apt-get clean all RUN pip install bottle # Add local files ADD hello.py /tmp/ # open a port EXPOSE 5000 # specify the default command to run CMD ["python", "/tmp/hello.py“] Dockerfile
  • 29. From Example of the what can you do with the docker file settings and lab setup
  • 30. Apache tomcat Apache Tomcat is a Java-based Web server. It is important in this context because it forms the basis for a number of key platforms, including DSpace, Fedora, and Islandora.
  • 31. Apache Airflow Apache Airflow is an open-source workflow management platform. It started at Airbnb in October 2014 as a solution to manage the company's increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface.
  • 32. DSpace DSpace is an open source repository software package typically used for creating open access repositories for scholarly and/or published digital content. Its design is focused on the long-term storage, access and preservation of digital content.
  • 35. Web Min Webmin is a Web-based system configuration tool for Unix-like systems, although recent versions can also be installed and run on Windows. Using any Web browser that supports tables and forms (and Java for the `File Manager` module), Webmin enables a user to administer a Linux or Unix system, e.g., setup user accounts, Apache, DNS, file sharing, etc., through a graphical user interface.
  • 36. nmap Nmap is a free and open-source network scanner created by Gordon Lyon. Nmap is used to discover hosts and services on a computer network by sending packets and analyzing the responses. Nmap provides a number of features for probing computer networks, including host discovery and service and operating system detection.
  • 37. metasploit The Metasploit Project is a computer security project that provides information about security vulnerabilities and aids in penetration testing and IDS signature development. It is owned by Boston, Massachusetts- based security company Rapid7.
  • 38. proxychains datasets - What are "proxy data sets" in machine learning? - Artificial Intelligence Stack Exchange
  • 43. What can it be in real world?
  • 44. Before that let me give you a quick introduction to the hacking 1. CND and CEH certification. 2. CCNA Certification. 3. OSCP Certification. 4. OFCP Certification. 5. CompTIA pentest
  • 45. There are lot of ways in which we can think about 1. Brute force attack 2. Dictionary attack 3. Hashing 4. Alternate data stream
  • 46. ●What is machine learning? ●Learning system model ●Training and testing ●Performance ●Algorithms ●Machine learning structure ●Learning techniques ●Applications An Overview of Machine Learning
  • 47. ●A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. ●As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. What is machine learning?
  • 49. ● Training is the process of making the system able to learn. ● No free lunch rule: ○ Training set and testing set come from the same distribution ○ Need to make some assumptions or bias Training and testing
  • 50. ●There are several factors affecting the performance: ○ Types of training provided ○ The form and extent of any initial background knowledge ○ The type of feedback provided ○ The learning algorithms used ●Two important factors: ○ Modeling ○ Optimization Performance
  • 51. ●The success of machine learning system also depends on the algorithms. ●The algorithms control the search to find and build the knowledge structures. ●The learning algorithms should extract useful information from training examples. Algorithms
  • 52. ●Supervised learning ○ Prediction ○ Classification (discrete labels), Regression (real values) ●Unsupervised learning ○ Clustering ○ Probability distribution estimation ○ Finding association (in features) ○ Dimension reduction ●Semi-supervised learning ●Reinforcement learning ○ Decision making (robot, chess machine) Algorithms
  • 57.
  • 58. ●SPAM detection ○ Distinguish between SPAM and HAM emails ○ number of emails correctly classified ○ Hand-labeled emails ●Detecting catalog duplicates ○ Distinguish between duplicate and non-duplicate catalog entries ○ False positive/negative rate based on business criteria ○ Hand-labeled duplicates and non-duplicates ●Go learner ○ Playing Go ○ number of games won in tournament ○ Practice games against itself Some Examples
  • 59. ●So many tools ○Preprocessing, analysis, statistics, machine learning, natural language processing, network analysis, visualization, scalability ●Community support ●“Easy” language to learn ●Both a scripting and production-ready language Programming Language - Why python?
  • 60. A very complete list can be found at PyPi the Python Package Index: https://pypi.python.org/pypi To install, use pip, which comes with Python: pip install package or download, unzip, and run the installer directly from the directory: python setup.py install External libraries
  • 61. ●Data analysis and modeling ●Similar to R and Excel, Keep everything in Python ●Easy-to-use data structures ○DataFrame ●Data wrangling tools ○Merging, pivoting, etc ●Use for preprocessing ○File I/0, cleaning, manipulation, etc ●Combinable with other modules ○NumPy, SciPy, statsmodel, matplotlib Pandas
  • 62. and there are some more easy libraries go to my repository here. vishwas 1234567/ML-On-Azure (github.com)
  • 63. ●Machine learning module ●Open-source ●Built-in datasets ●Good resources for learning ●Very comprehensive of machine learning algorithms ●Preprocessing tools ●Methods for testing the accuracy of your model Scikit-learn
  • 64. ●Natural Language ToolKit ●Access to over 50 corpora ○Corpus: body of text ●NLP tools ○Stemming, tokenizing, etc ●Resources for learning ○Lemmatizing, tokenization, tagging, parse trees ○Classification ○Chunking ○Sentence structure nltk
  • 65. https://www.crummy.com/software/BeautifulSoup/ Web analysis. Need other packages to actually download pages like the library requests. http://docs.python-requests.org/en/master/ BeautifulSoup navigates the Document Object Model: http://www.w3schools.com/ Not a library, but a nice intro to web programming with Python. https://wiki.python.org/moin/WebProgramming Beautiful Soup
  • 66. Just do one command pip install zope
  • 67. important resources My channel has podcasts thus do see my podcasts on youtube and also some community tutorials that i will be taking. Vishwas Narayan - YouTube
  • 68. For other resources stay tuned for the series Mark the playlist that is shared in the mail and also get the things moved in as the things are going to be
  • 69. Questions Is everyone Still Alive and seeing me online???????