Slides (pdf) - Jesse Kornblum

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Clustering• Can find clusters of similar programs– Unsupervised machine learning– Artificial intelligence– There are many algorithms• Start with pile of programs• Press “go”• End up with clusters of similar programs• Example:– Programs A, B, C, D, E, F, and G28
• Each program belongs to at most one clusterExclusive Clusters• Not all programs in a cluster are similar to each other• Some programs are not similar to any others– Unique programs29
Page 1 and 2: Clustering and Classification forCy
Page 3 and 4: • Kyrus- Winston Wolf ofcomputer
Page 5 and 6: Similarity• What does it mean for
Page 7 and 8: Example7
Page 9 and 10: Current Tools• Cryptographic Hash
Page 11 and 12: Fuzzy Hashing• Compare signatures
Page 13 and 14: • Better algorithm for matching s
Page 15 and 16: Similar Programs• Similarity depe
Page 17 and 18: Features• What features can we ex
Page 19 and 20: • Clustering- Group of inputs whi
Page 21 and 22: Distance Measures21
Page 23 and 24: Distance MeasuresEntropyRepresent f
Page 25 and 26: Cosine SimilarityEntropyθThe small
Page 27: Comparisons• Can find programs si
Page 31 and 32: Clustering vs. ClassificationCluste
Page 33 and 34: Classification• User must create
Page 35 and 36: • Also used for spam detector- Al
Page 37 and 38: • Quick to classify, but slow to
Page 39 and 40: • Precision measures false positi
Page 41 and 42: • All of these are Free and Open
Page 43 and 44: Conclusion• Analyzing an infinite
Page 45: Questions?Jesse Kornblumjesse.kornb

Slides (pdf) - Jesse Kornblum

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