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Machine Learning for Cyber Security
Unit : IOT device detection
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Learning Objectives Taking a problem ML pipeline Raw data ML
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IOT Device Detection IOT = Internet of Things Alexa Assistant IP Misc
Network Mobile ? Power Outlet camera IP TCP/IP Assistant Alexa Misc
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What do we know? Classes? supervised? What are the features?
unsupervised? How much data do I have? Label? Vector Space Data
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IOT data(40 features) Router F1 F2 F3 IP/TCP fields C S1 S2 S3 X Y F
X Y F SW S1 S2 Alexa Camera TCP/IP ✘ UDP/IP IP/TCP Network data pcap(Wireshark, TCP dump, libpcap)
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Packet Eth IP TCP Payload ✘ ✓ ✓ feature
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Sample Per packet basic Sample Window of time basic
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Graph demo Vector space ML Data clean Evaluate
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classes Supervised Identity MAC address Source IP Unsupervised
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TCP/IP headers Source IP, if IP in camera_IPs, class=0 F1 F2 F40 S1 S2
Source IP, if IP in camera_IPs, class=0 X Y
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Tshark and Python Vector space ML Data clean Evaluate WEKA
Feature extraction
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Lab Goal is to extract features from pcap file Identify the feature
Write a python script List of IPs per IOT device for the classes 18 features
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Lab environment Python Tshark WEKA Pcap Starter code
File Starter code Evaluate performance ML Select 3 algorithms Evaluate
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Feature extraction Camera.pcap P1 P2 Camera Server Host Assistant
To use pcap Misc Mobile List of IPs Camera Server Host Alexa P1 P2
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Graph Class Data vector Camera.pcap P1 features Camera /P1 P2
Vector space Camera.pcap /P1 P2 ML
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Now we proceed to the code
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Summary IOT Device Detection Lab Realistic data set Big data
Raw data => VSM ML pipeline WEKA
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