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A DIFFERENT APPROACH - PROBABILISTIC INFERENCE Rather than running algorithms the model the shape of data, we need to take expert knowledge/ domain expertise into account
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Classroom Contents
AI & ML in Cyber Security - Why Algorithms Are Dangerous
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- 1 Intro
- 2 RAFFAEL MARTY
- 3 OUTLINE
- 4 ML AND AI - WHAT IS IT? MACHINE LEARNING Algorithmic ways to describe data Supervised
- 5 MACHINE LEARNING USES IN SECURITY
- 6 FAMOUS AI (ALGORITHM) FAILURES
- 7 WHAT MAKES ALGORITHMS DANGEROUS? ALGORITHMS MAKE ASSUMPTIONS ABOUT THE DATA
- 8 COGNITIVE BIASES
- 9 THE DANGERS WITH DEEP LEARNING - WHEN NOT TO USE IT
- 10 ADVERSARIAL MACHINE LEARNING
- 11 DEEP LEARNING - THE SOLUTION TO EVERYTHING
- 12 UNSUPERVISED TO THE RESCUE?
- 13 UNDERSTAND AND CLEAN THE DATA
- 14 ENGINEERING DISTANCE FUNCTIONS
- 15 CHOOSING THE RIGHT UNSUPERVISED ALGORITHM
- 16 CHOOSING THE CORRECT ALGORITHM PARAMETERS
- 17 INTERPRETING THE DATA
- 18 A DIFFERENT APPROACH - PROBABILISTIC INFERENCE Rather than running algorithms the model the shape of data, we need to take expert knowledge/ domain expertise into account
- 19 ST STEP-BUILD THE GRAPH
- 20 ND STEP - GROUP NODES
- 21 RD STEP - INTRODUCE DEPENDENCIES
- 22 TH STEP - ESTIMATE PROBABILITIES
- 23 TH STEP-GOAL COMPUTATION
- 24 TH STEP-OBSERVE ACTIVITIES
- 25 TH STEP-EXPERT INPUT Strengthen the network by introducing expert knowledge
- 26 BELIEF NETWORKS - SOME OBSERVATIONS
