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Malware Detection using Machine Learning

This document discusses using machine learning for malware detection. It begins with an introduction to machine learning and feature selection for classification problems. Popular supervised learning algorithms that can be used for malware detection are discussed, including K-Nearest Neighbors (KNN), K-Means clustering, Support Vector Machines (SVM), Decision Trees, and Random Forests. Features that could be used for malware detection, such as file size, entropy, and behavioral patterns, are also outlined. The document concludes with brief mentions of neural networks, deep learning, and Python libraries that can be used like Scikit-Learn.

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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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In this document
Powered by AI

Overview of malware detection with a focus on machine learning; presenter background and references used.

Traditional antivirus methods and malware evasion techniques, highlighting the need for machine learning due to vast data.

Basics of machine learning, including prerequisites and types, focusing on classification problems.

Key steps in supervised learning with emphasis on feature selection, training with labeled data, and prediction.

Details on feature selection to distinguish classes, with examples and various model options for supervised learning.

Overview of KNN as a supervised classification algorithm, discussing similarity measures and classification process.

Introduction to K-Means as an unsupervised learning clustering algorithm and process of updating mean values.

Explanation of SVMs, including support vectors, hyperplanes, and margin concepts.

Introduction to decision trees and the ensemble learning method of random forests for classification.

Static and behavioral features relevant for malware detection, including training sets and DGA algorithm.

Brief mention of advanced topics such as neural networks and deep neural networks for malware detection.

Listing Python libraries useful for machine learning such as Scikit-Learn, Numpy, and Pandas.

Malware Detection using Machine Learning

  • 1.
  • 2.
    ABOUT PRESENTER • Workedas 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 usedsome 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 inAntivirus: 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: • Whatis it? • Steps: – Feature Selection – Training(provide Labelled Data) – Prediction
  • 7.
    FEATURE SELECTION • Howfeatures 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
  • 9.
    MODEL SELECTION Models forsupervised Learning: •K-Nearest Neighbours(KNN)-classification •K-Means clustering •SVM •Decision Tree •Random Forest •Naive Bayes Algorithm
  • 10.
    K-Nearest Neighbours(KNN) • Supervisedlearning • 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
  • 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
  • 19.
  • 20.
    Random Forest • Ensemblelearning 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.
  • 23.
    Some application forMalware Traffic Detection • DGA algorithm detection • DGA: what is DGA? • Features: – N-Grams – Entropy – Dictionary – Reference:http://datadrivensecurity.info
  • 24.
    ADVANCED TOPICS • NEURALNETWORKS • DEEP NEURAL NETWORKS
  • 25.