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Malware Detection using Machine Learning

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Malware Detection using Machine Learning

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MALWARE DETECTION USING MACHINE LEARNING ABHIJIT MOHANTA
ABOUT PRESENTER • Worked as security researcher for Symantec,Mcafee,Cyphort • Experience in reverse engineering ,malware analysis and detection • Worked on antivirus engines,and sandbox engines
DISCALIMER I have used some contents from the following sites Reference: • analyticsvidhya.com • datadrivensecurity.info • home.agh.edu.pl • neuralnetworksanddeeplearning.com • http://www.astroml.org • Youtube • Google images
Malware Detection in Antivirus: How Antiviruses detect malware? • Traditional AV's pattern matching on static files • Partially decrypt using techniques like emulation How Malwares evade antivirus? • use polymorphic packers which evades static pattern matching Why Machine Learning? • Too many types of malware bots,virus • Based on target stealers,POS malwares,banking • Too much data for human to process
MACHINE LEARNING INTRO • Some prerequisites: statistics,calculus,vectors,algebra • Problems solved: classification /regression • Types: supervised,semi- supervised,unsupervised • What is our problem? Classification
Supervised Learning: • What is it? • Steps: – Feature Selection – Training(provide Labelled Data) – Prediction
FEATURE SELECTION • How features are selected in Classification? • Some property with which you can distinguish two classes is A Feature • Feature can be represented as Vector,Boolean etc • Apple Vs Orange Class: – Feature: colour,weight,shape – Label: apple,guava
MODEL SELECTION Models for supervised Learning: •K-Nearest Neighbours(KNN)-classification •K-Means clustering •SVM •Decision Tree •Random Forest •Naive Bayes Algorithm
K-Nearest Neighbours(KNN) • Supervised learning • Classification Algorithm • Similarity to neighbours-(Eucledian,Manhattan,Minkowski) • Euclidean distance • A circle around the point to be classified that contains k points
K-Means • Unsupervised learning • Clustering algorithm • Given some data we cluster the data to K groups • In each iteration the mean value of the cluster is updated • Centre calculated using Eucledian distance • ref video:https://www.youtube.com/watch? v=aiJ8II94qck
Support Vector Machines • Classifier • What are support vectors • Linearly separating Hyperplane • Margins with max separation
Support Vector Machines • ref:http://www.saedsayad.com/support_vector_machine.htm • videos: • https://www.youtube.com/watch?v=1NxnPkZM9bc • https://www.youtube.com/watch?v=5zRmhOUjjGY
Decision Tree Ref:https://databricks.com/blog/2014/09/29/scalable-decision-trees-in- mllib.html
Random Forest • Ensemble learning method • Uses output of multiple decision trees Ref:https://citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics/
Features for Malware Detection • Static: – Size – Signed/unsigned – Icon-exe file without icons – entropy • Behaviour: – Process executed from %appdata% and %temp% – Dropped file has random name eg xszsde.exe – Process creating run entries – Code injection
Training Sets for malware
Some application for Malware Traffic Detection • DGA algorithm detection • DGA: what is DGA? • Features: – N-Grams – Entropy – Dictionary – Reference:http://datadrivensecurity.info
ADVANCED TOPICS • NEURAL NETWORKS • DEEP NEURAL NETWORKS
PYTHON LIBRARIES • Scikit-Learn • Numpy • Pandas
Malware Detection using Machine Learning

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Malware Detection using Machine Learning

  • 2. ABOUT PRESENTER • Worked as security researcher for Symantec,Mcafee,Cyphort • Experience in reverse engineering ,malware analysis and detection • Worked on antivirus engines,and sandbox engines
  • 3. DISCALIMER I have used some contents from the following sites Reference: • analyticsvidhya.com • datadrivensecurity.info • home.agh.edu.pl • neuralnetworksanddeeplearning.com • http://www.astroml.org • Youtube • Google images
  • 4. Malware Detection in Antivirus: How Antiviruses detect malware? • Traditional AV's pattern matching on static files • Partially decrypt using techniques like emulation How Malwares evade antivirus? • use polymorphic packers which evades static pattern matching Why Machine Learning? • Too many types of malware bots,virus • Based on target stealers,POS malwares,banking • Too much data for human to process
  • 5. MACHINE LEARNING INTRO • Some prerequisites: statistics,calculus,vectors,algebra • Problems solved: classification /regression • Types: supervised,semi- supervised,unsupervised • What is our problem? Classification
  • 6. Supervised Learning: • What is it? • Steps: – Feature Selection – Training(provide Labelled Data) – Prediction
  • 7. FEATURE SELECTION • How features are selected in Classification? • Some property with which you can distinguish two classes is A Feature • Feature can be represented as Vector,Boolean etc • Apple Vs Orange Class: – Feature: colour,weight,shape – Label: apple,guava
  • 8.
  • 9. MODEL SELECTION Models for supervised Learning: •K-Nearest Neighbours(KNN)-classification •K-Means clustering •SVM •Decision Tree •Random Forest •Naive Bayes Algorithm
  • 10. K-Nearest Neighbours(KNN) • Supervised learning • Classification Algorithm • Similarity to neighbours-(Eucledian,Manhattan,Minkowski) • Euclidean distance • A circle around the point to be classified that contains k points
  • 11. K-Means • Unsupervised learning • Clustering algorithm • Given some data we cluster the data to K groups • In each iteration the mean value of the cluster is updated • Centre calculated using Eucledian distance • ref video:https://www.youtube.com/watch? v=aiJ8II94qck
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  • 17. Support Vector Machines • Classifier • What are support vectors • Linearly separating Hyperplane • Margins with max separation
  • 18. Support Vector Machines • ref:http://www.saedsayad.com/support_vector_machine.htm • videos: • https://www.youtube.com/watch?v=1NxnPkZM9bc • https://www.youtube.com/watch?v=5zRmhOUjjGY
  • 20. Random Forest • Ensemble learning method • Uses output of multiple decision trees Ref:https://citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics/
  • 21. Features for Malware Detection • Static: – Size – Signed/unsigned – Icon-exe file without icons – entropy • Behaviour: – Process executed from %appdata% and %temp% – Dropped file has random name eg xszsde.exe – Process creating run entries – Code injection
  • 22. Training Sets for malware
  • 23. Some application for Malware Traffic Detection • DGA algorithm detection • DGA: what is DGA? • Features: – N-Grams – Entropy – Dictionary – Reference:http://datadrivensecurity.info
  • 24. ADVANCED TOPICS • NEURAL NETWORKS • DEEP NEURAL NETWORKS