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1. botnet detection algorithms and techniques

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1. botnet detection algorithms and techniques

  1. 1. C&C botnet detection algorithms and techniques Presented By: Djona Fegnem
  2. 2. What is the Botnet ! Bot – A malware instance that runs autonomously on a compromised computer without owner consent. ! Botnet (Bot army): network of bots controlled by a Botmaster. ! Botmaster : individual who architect and control the botnet. ! C&C server Computer used to coordinate the actions of computers infected by a bot.
  3. 3. Botnet DDOS Attack
  4. 4. Motivations ! Botnets are enabler of following threats: ! DDOS ! Spam ! Click Fraud ! Information theft ! Phishing attacks ! Distributing Malware ! 25% in the world are part of the bot (Vint – Cerf ) ! Reports are alarming ! Q4 Mcafee reports (2012) ! Internet Security threat report 2014
  5. 5. Motivations ! Traditional detection approaches failed (Signature based approach) ! Signature based approach ! Collect botnet samples ! Analyze samples ! Extract Behavior ! Generate and deploy detection model. ! Problem Hard ! Lack of general definition of botnet behavior. ! Attackers have much freedom. ! Need a new approach ! Machine Learning a rescue.
  6. 6. Challenges ! Selection of network Monitoring tool ! Features selections ! Machine Learning algorithm selection ! False positive ! The fast flux
  7. 7. C&C approaches ! Statistical approach ! Monitor traffics to extract samples data or use an existing one. ! Extract relevant features. ! Choose the machine learning algorithms and tools. ! Train the system with data. (some heuristics can be apply during training) ! Generate and deploy the detection model. ! Behavioral approach ! Monitor an individual host to identify specific host behavior. e.g: surfing habit. ! Use some heuristics or machine learning algorithms ! Generate the host of behavior pattern ! Correlation approach ! Flow – Based correlations: correlate traffic flow ! Flow and behavioral correlation : correlate traffic flow and activities.
  8. 8. Machine Learning Algorithms ! C4.5 ! Random Forest ! Support Vector Machine (SVM) ! Artificial Neural Network (ANN) ! K Nearest Neighbors
  9. 9. Statistical approach: C4.5 Algorithm ! Decision tree algorithm. ! Developed by Ross Quinlan (1993) ! Use the concept of entropy (degree of purity) and information gain to build the tree. ! Used for C&C detection: ! Reference [1], [2], [5]
  10. 10. Statistical approach: C4.5 Algorithm intuition Decision Tree
  11. 11. Statistical approach: Random Forest ! Ensemble classifier using many decision tree models ! Can be used for classification or regression ! Given a data set, it builds K decision trees by selecting a random subset of data ! During prediction, random forest uses a majority of vote: class predicted most often. ! Used in botnet detection ! Reference [2]
  12. 12. Statistical approaches: Random Forest
  13. 13. Statistical approaches: Support Vector Machine ! Binary machine learning classifier. ! Build a hyperplane that optimally separates samples of data with maximal margin ! Can be extended to K-class classification by constructing k two class classifiers ! Use a non-linear mapping to solve a non linear classification problem. ! Use for detection ! Reference : [3] , [4]
  14. 14. Statistical approaches: Support Vector Machine
  15. 15. Statistical approaches: Artificial Neural Network ! Machine learning algorithm use for classification ! Models the brain and the nervous system ! Composed of many “neurons” that co-operate to perform the desired function. ! Use for detection: ! Reference [2]
  16. 16. Statistical approach: Artificial Neural Network
  17. 17. Statistical approach: K Nearest Neighbors ! Machine learning classifier. ! Classify an instance by finding its k nearest neighbors ! Pick the most popular class among the neighbors. ! Use for detection ! Reference [2]
  18. 18. Behavioral approaches ! Monitor an individual host to identify specific host behavior. e.g: surfing habit. ! Use some heuristics or machine learning algorithms ! Generate the host of behavior pattern ! Use for detection: ! Reference [6]
  19. 19. Correlation Approach ! Flow correlation ! Correlation based approach that correlate network traffic flow to C&C flow ! Reference [7] ! Flow and activities correlation ! Combine flow correlation with activity-based correlation of host. ! Reference [8]
  20. 20. References [1]On Botnet Behaviour Analysis using GP and C4.5 Fariba Haddadi, Dylan Runkel, A. Nur Zincir-Heywood, and Malcolm I. Heywood Faculty of Computer Science Dalhousie UniversityHalifax, Nova Scotia, Canada [2] An efficient flow-based botnet detection using supervised machine learning Matija Stevanovic and Jens Myrup Pedersen Networking and Security Section, Department of Electronic Systems Aalborg University, Fredrik Bajers Vej 7,DK-9220 Aalborg, Denmark. [3]Analyzing String Format-Based Classifiers For Botnet Detection: GP and SVM Fariba Haddadi, A. Nur Zincir-Heywood Computer Science, Dalhousie University Halifax, NS, Canada [4] Kazumasa Yamauchi, Yoshiaki Hori, Kouichi Sakurai , Detecting HTTP-based Botnet based on Characteristic of the C&C session using by SVM.
  21. 21. References [5] Feature Selection for Detection of Peer-to-Peer Botnet Traffic Pratik Narang, Jagan Mohan Reddy, Chittaranjan Hota Department of Computer Science & EngineeringBirla Institute of Technology and Science-Pilani, Hyderabad Campus Shameerpet, R.R. District, A.P., India 500078 [6] P. Wurzinger, L. Bilge, T. Holz, J. Gobel, C. Kruegel, and E. Kirda, “Automatically generating models for botnet detection,” in European Symposium on Research in Computer Security (ESORICS), 2009. [7] T. Strayer, D. Lapsley, R. Walsh, and C. Livadas, Botnet detection based on network behavior, ser. Advances in Information Security. Springer, 2008, vol. 36, pp. 1–24. [8] G. Gu, R. Perdisci, J. Zhang, and W. Lee, “Botminer: Clustering anal- ysis of network trafficfor protocol- and structure-independent botnet detection,” in Usenix Security Symposium, 2008. [9]A Taxonomy of Botnet Behavior,Detection, and Defense Sheharbano Khattak, Naurin Rasheed Ramay, Kamran Riaz Khan, Affan A. Syed, and Syed Ali Khayam
  22. 22. Thanks

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