1. Machine Learning Speaker :Chia-Shing Huang Advisor :Dr. Kai-Wei Ke 2016/01/14 1

2. Outline  Machine learning  Decision tree  Artificial neural Network  Conclusion 2

3. Machine Learning Definition  Field of study that gives computers the ability to learn without being explicitly programmed - Arthur SamuelArthur Samuel  A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E - Tom M. MitchellTom M. Mitchell 3

4. Simple Learning Flow 4

5. Method  Supervised learning  Unsupervised learning  Semi-supervised learning  Reinforcement learning 5

6. Decision Tree What time is it? Has homework? Has date? N YYN Play game or not? < 19:00 >19:00 false true false true A decision tree is a flowchart-like structure in which each internal node represents a "test" on an, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represents classification rules. 6

7. Classification and Regression Tree(CART)  Number of branches = 2 (binary tree)  Base hypothesis = optimal constant  Binary/multiclass classification(0/1 error) : majority of {yn} (result)  Regression(squared error) : average of {y n } (result)  Termination criteria = until forced to terminate  All y n the same  All x n the same 7

8. decision stumps h(x) Data rate in total data 8

9. Simple Data Set One more example Let’s play online ! http://cn.akinator.com 9

10. Artificial Neural Network (ANN)  Definition: Artificial neural networks (ANNs) are a family of models inspired by biological neural networks and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. 10

11. Single Neuron XnXn X1X1 X2X2 X3X3 X0X0 SUM Transform Function F output w0w0 w1w1 w2w2 w3w3...wn...wn X i = nonlinear information (input) W i = weight of data features Perceptron Algorithm 11

12. Hidden layer 12

13. XnXn X1X1 X2X2...... w1w1 w2w2 g2g2 g1g1 +1 X 0 = 1 +1 13

14. w1w1 w2w2 w3w3 wnwn...... XnXn X1X1 X2X2 X3X3 b g2g2 g1g1 gngn G............ a1a1 a2a2 a3a3...an...an Fee dforward Network Feedback Network How to get optimization? Use Gradient descent 14

15. Example :DDoS attack detection  Distributed Denial of Service(DDos) attack: is an attempt to make an online service unavailable by overwhelming it with traffic from multiple sources.  SYN flood  UDP Flood  ICMP Flood  LAND attack 15

16. Example :DDoS attack detection(con’t)  Training data  CPU idle rate  Memory usage  Network packets inflows  Network packet outflows  Current number of system process  Ideal target (normal =0 /attack = 1) 16

17. i j 17

18. 18 Schematic Simulation Environment

19. 19 Simulation Environment Hardware Standard

20. Artificial Neural Network Preferences  Input = 5 neurons  CPU idle rate  Memory usage  Network packets inflows  Network packet outflows  Current number of system process  Hidden layer = 10 neurons  Output = 1 neuron (true or false)  Weight & threshold = random (0~1) 20

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25. Conclusion - Decision tree Pros:  Human-explainable, widely used in business/medical data analysis  Simple  Efficient in prediction and training Cons:  Heuristic: mostly little theoretical explanations  Confusing to beginners 25

26. Conclusion - Artificial Neural Network Pros:  good to model the non-linear data with large number of input features  Robustness & fault-tolerance  Strong adaptability Cons:  So many answers that can’t identify which is the best answer.  are prone to overfitting  requires greater computational resources 26

27. Reference  http://supercomputer.ncku.edu.tw/ezfiles/343/1343/img/1609/125202900.p df http://supercomputer.ncku.edu.tw/ezfiles/343/1343/img/1609/125202900.p df  https://www.youtube.com/watch?v=nQvpFSMPhr0&list=PLXVfgk9fNX2I7tB6oII NGBmW50rrmFTqf https://www.youtube.com/watch?v=nQvpFSMPhr0&list=PLXVfgk9fNX2I7tB6oII NGBmW50rrmFTqf  https://class.coursera.org/ntumltwo-002/lecture https://class.coursera.org/ntumltwo-002/lecture  http://bryannotes.blogspot.tw/2014/11/algorithm-stochastic- gradient_4.html http://bryannotes.blogspot.tw/2014/11/algorithm-stochastic- gradient_4.html  https://en.wikipedia.org/wiki/Decision_tree https://en.wikipedia.org/wiki/Decision_tree  https://en.wikipedia.org/wiki/Artificial_neural_network https://en.wikipedia.org/wiki/Artificial_neural_network  Ashraf, J. and Latif, S., “Handling intrusion and DDoS attacks in Software Defined Networks using machine learning techniques” in National Software Engineering Conference (NSEC), 2014,pp. 55-60.  紀宏宜 、 張偉德 、 陳志榮, “ 應用類神經網路於阻斷式服務攻擊之預測 ” 網際網 路技術學刊, pp.173-178, 9:2 2008.04[ 民 97.04] 27

28. Thank you for listening Happy winter vacation & happy new year 28

29. w1w1 w2w2 w3w3 wnwn...... w0w0 XnXn X1X1 X2X2 X3X3 X0X0 g2g2 g1g1 g0g0 gngn G............ a0a0 a1a1 a2a2 a3a3...an...an 29

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