PPT-Hidden Markov Models for Software Piracy Detection

Shabana Kazi Mark Stamp HMMs for Piracy Detection 1 Intro Here we apply metamorphic analysis to software piracy detection Very similar to techniques used in malware detection But problem is completely different . (1). Brief . review of discrete time finite Markov . Chain. Hidden Markov . Model. Examples of HMM in Bioinformatics. Estimations. Basic Local Alignment Search Tool (BLAST). The strategy. Important parameters. Van Gael, et al. ICML 2008. Presented by Daniel Johnson. Introduction. Infinite Hidden Markov Model (. iHMM. ) is . n. onparametric approach to the HMM. New inference algorithm for . iHMM. Comparison with Gibbs sampling algorithm. Johnny Lam. Randi Wagner. Davina. . Vilaylak. Introduction. Online piracy is associated with copyright infringement. Copyright infringement is defined as unauthorized distribution or use of copyrighted works. By Eric . Kutcher. Overview. Piracy is considered a detriment to commercial businesses insofar as it pertains to revenue loss. There are some (pirates) who feel it is a good thing.. As all things on a computer are digital, copying a product such as a movie, CD, or software application can be done by any single person and at a cost that’s relatively cheap.. Group #6. Stephen Gaither. Chad Andrus. Louis Russo. Daniel . Ascencio. Brad . Gattuso. . A. Global Issue. What is Computer Piracy?. The unauthorized use of material that is covered by copyright law.. By: Ashley, Sabiha, and Roshaan. Software Piracy is . the . unauthorized copying of software. .. What is it?. By buying the software, you become a licensed user rather than an owner . You are allowed to make copies of the program for backup purposes, but it is against the law to give copies to friends and colleagues.. First – a . Markov Model. State. . : . sunny cloudy rainy sunny ? . A Markov Model . is a chain-structured process . where . future . states . depend . only . on . the present . state, . notes for. CSCI-GA.2590. Prof. Grishman. Markov Model . In principle each decision could depend on all the decisions which came before (the tags on all preceding words in the sentence). But we’ll make life simple by assuming that the decision depends on only the immediately preceding decision. in . association with . Egersund. Conference. 10. th. December 2009. Arild Nodland, CEO. Incorporated 16 January 2007. Arild . Nodland, founder & CEO. . 16 . years analysing conflicts and high risk countries. Mark Stamp. 1. HMM. Hidden Markov Models. What is a hidden Markov model (HMM)?. A machine learning technique. A discrete hill climb technique. Where are . HMMs. used?. Speech recognition. Malware detection, IDS, etc., etc..  ... Recent . research by . ipsos. suggest that . amlost. 30% of the UK population is active in some form of piracy. This cost the . Uk. audio visual industries about £500M a year. When a film is copied or made available online it reduces the value of that film around the world. Hidden Markov Models Teaching Demo The University of Arizona Tatjana Scheffler tatjana.scheffler@uni-potsdam.de Warm-Up: Parts of Speech Part of Speech Tagging = Grouping words into morphosyntactic types like noun, verb, etc.: BMI/CS 776 . www.biostat.wisc.edu/bmi776/. Spring . 2018. Anthony Gitter. gitter@biostat.wisc.edu. These slides, excluding third-party material, are licensed . under . CC BY-NC 4.0. by Mark . Craven, Colin Dewey, and Anthony Gitter. Markov processes in continuous time were discovered long before Andrey Markov's work in the early 20th . centuryin. the form of the Poisson process.. Markov was interested in studying an extension of independent random sequences, motivated by a disagreement with Pavel Nekrasov who claimed independence was necessary for the weak law of large numbers to hold..