Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College


 Jonas Andrews
 2 years ago
 Views:
Transcription
1 Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College
2 Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure Solvng the optmzaton problems 2/50
3 The feature selecton problem The curse of dmensonalty 2 close ponts n a 2 dmensonal space are probably dstant n a 100 dmensonal space Any machne learnng algorthm Makes a predcton of unseen data ponts by a hypothess constructed from a lmted number of tranng nstances In hgh dmensonal space dffcult 3/50
4 The feature selecton problem Hypothess (n ths settng) A pattern or functon that predcts classes based on gven data Hypothess space Contans all the hypotheses that can be learned from data 4/50
5 The feature selecton problem A lnear ncrease n the number of features (.e. the dmenson of the feature space) leads to the exponental ncrease of the hypothess space Example: 2 classes, N bnary features The cardnalty of the hypothess space: N 2 2 5/50
6 The feature selecton problem Feature selecton Removes rrelevant features Removes redundant features Consequence Effcent reducton of the hypothess space Easer to fnd the correct hypothess Reduced number of requred tranng nstances (the reducton s exponental) 6/50
7 The feature selecton problem Removng rrelevant features Does not affect learnng performance Removng redundant features Redundant features a type of rrelevant features The dfference: a redundant feature requres co presence of another feature Each ndvdual feature s relevant, but removal of one of them wll not affect learnng performance 7/50
8 The feature selecton problem 2 types of feature selecton methods Feature rankng Rank features accordng to some crteron and select the top k features A threshold s needed n advance to select the top k features Feature subset selecton Selects the mnmum subset of features that does not deterorate learnng performance No threshold necessary 8/50
9 The feature selecton problem Models of feature selecton The flter model Consders statstcal propertes of a data set drectly No learnng algorthm nvolved Effcent The wrapper model Performance of a gven learnng algorthm s used to determne the qualty of selected features 9/50
10 Intruson detecton Intruson Actvtes amed at volatng securty (.e. confdentalty, ntegrty and avalablty of computer and network resources) Intruson detecton Process of detecton and dentfcaton of attacks Intruson preventon Process of attack detecton and defense management 10/50
11 Intruson detecton Intruson detecton system IDS A system that automatcally detects attacks aganst hosts and networks Intruson preventon system IPS A system, whose ambton s to detect attacks and manage defence actvtes An IPS contans an IDS IPS combne IDS wth other preventve measures (frewall, ant vrus, vulnerablty scannng, etc.) 11/50
12 Intruson detecton IDS classfcaton Accordng to the protected object Host based IDS Network based IDS Accordng to the detecton model Msuse detecton IDS Anomaly detecton IDS 12/50
13 Intruson detecton Host based IDS Collect data from nternal sources, usually at the operatng system level (varous logs) Montor user actvtes Montor executon of system programs 13/50
14 Intruson detecton Network based IDS Collect packets, usually by means of network nterfaces n so called promscuous mode (such a devce collects all the packets that reach the nterface, not only those addressed to the host) Perform analyss of the collected packets Montor network actvty 14/50
15 Intruson detecton Msuse detecton systems Collect nformaton about attack ndcators and then determne whether those ndcators are present n ncomng data Attack ndcators (sgnatures) Analyss (e.g. pattern matchng) Attack Actvtes 15/50
16 Intruson detecton Anomaly detecton systems Defne profles of normal behavour of users or networks, compare actual behavour wth those profles and generate alerts f the dscrepancy from the profles s too hgh Profles of normal behavour Analyss Attack Actvtes 16/50
17 Intruson detecton Incomng traffc/logs Data preprocessor Actvty data Detecton model(s) Detecton algorthm Alerts Decson crtera Alert flter Acton/report 17/50
18 Intruson detecton What propertes should the data preprocessor possess? Whch detecton model s optmal? What s the best detecton algorthm? What are the optmal decson crtera? What alert flter gves the best results? 18/50
19 Intruson detecton Untl recently, the answers to those questons were heurstc ntruson detecton was a more techncal dscplne, wthout clear theoretcal foundaton After 2005, some theoretcal models of IDS appeared Models based on complexty theory Informaton theoretc models 19/50
20 Intruson detecton IDS model (1) IDS s an 8 tuple (,Σ,,,,, ) The frst 4 components are data structures Data source The set of data states Σ The set of data unt features Knowledge base about data profles 20/50
21 Intruson detecton IDS model (2) The second 4 components are algorthms Algorthm for feature selecton Algorthm for reducton and representaton Knowledge base generator Classfcaton algorthm 21/50
22 Intruson detecton Data source A flow of consecutve data unts (packets, data flow unts, system calls) = (D 1, D 2,...), where D s the analyzed data unt, D {d 1, d 2,...}, d j s a possble data unt In network based IDS, s a stream of packets P=(P 1, P 2,...) In host based IDS, can be a stream of system calls C=(C 1, C 2,...) 22/50
23 Intruson detecton The set of data states Σ Contans normalty ndcators for each D If D s abnormal, t s possble that the correspondng ndcator from Σ also contans the type of the attack In anomaly detecton, Σ={normal, abnormal} or Σ={N,A} or Σ={0,1} In msuse detecton, Σ={normal, attack type 1, attack type 2,...} or Σ={N,A 1,A 2,...} 23/50
24 Intruson detecton The set of data unt features A vector of features that contans a fnte number of attrbutes of a data unt, F=(f 1,f 2,...,f n ) Examples: protocol name, port number, etc. Every feature has ts doman R A set of dscrete or contnuous values 24/50
25 Intruson detecton Knowledge base about data profles Contans profles of normal and abnormal data unts Internal structure of the base s dfferent for each IDS (a tree, a Markov model, a Petr net, a set of rules, a base of attack sgnatures, etc.) In msuse based systems, s a set of rules that descrbe attack profles (.e. attack sgnatures) In anomaly detecton systems, s a profle of normal traffc 25/50
26 Intruson detecton An deal data unt tester Oracle IDS Performs analyss of each data unt D Gves the ndcator value at the output Normal Abnormal Always gves the correct value of the ndcator For each D, ts state s Oracle IDS (D ) 26/50
27 Intruson detecton Algorthm for feature selecton Gven and the correspondng states from Σ, the algorthm gves certan number of features that IDS wll measure and decde on them In general, depends very much on the knowledge about the attack characterstcs The qualty of the selected features manly determnes the effectveness of the IDS 27/50
28 Intruson detecton Feature selecton 28/50
29 Intruson detecton Algorthm for reducton and representaton Durng data processng, IDS frst performs data reducton,.e. extracton of characterstcs that are the results of the executon of the algorthm, and then ther representaton n the form of a vector wth coordnates n Thus, : 29/50
30 Intruson detecton Knowledge base generator To generate the knowledge base, we need an algorthm that, based on the vectoral data representatons and ther states, generates the knowledge base 30/50
31 Intruson detecton Knowledge base generaton 31/50
32 Intruson detecton Classfcaton algorthm That s a functon that maps the representaton of the gven data unt nto some state, based on the knowledge base Formally, : Σ 32/50
33 Intruson detecton Detecton procedure (classfcaton) 33/50
34 Intruson detecton Phases of operaton of an IDS (1) Feature selecton In general, ths phase s executed only once, durng the development of the IDS Knowledge base generaton Sometmes called the tranng procedure The algorthm (wth the help of the algorthm ) s executed over a large quantty of tranng data In general executed once, but the base may occasonally be updated 34/50
35 Intruson detecton Phases of operaton of an IDS (2) Detecton procedure IDS s appled over real data n order to detect attacks The most mportant and most often used phase 35/50
36 Traffc features relevant for IDS The goal of the feature selecton algorthm n an IDS To determne the most relevant features of the ncomng traffc, whose montorng ensures relable detecton of abnormal behavor Effectveness of the classfcaton heavly depends on the number of features It s necessary to mnmze that number, wthout droppng ndcators of abnormal behavor 36/50
37 Traffc features relevant for IDS In the contemporary IDS The most of work on feature selecton s stll done manually The feature selecton depends too much on expert knowledge unrelable Better algorthms for automatc feature selecton n IDS are needed 37/50
38 Traffc features relevant for IDS For IDS Due to hgh dmensonal data, the flter model s more approprate for automatc feature selecton To elmnate redundant features, the featuresubset evaluatng method seems to be better than the feature rankng method A generc feature selecton measure s defned frst and then the methods to maxmze t are found 38/50
39 Traffc features relevant for IDS The generc feature selecton measure (*) = 1 ( X) ( X) x =1 ndcates appearance of the feature f a 0 and b 0 are constants A (X) and B (X) are lnear functons The feature selecton problem n a0 + A x = 1 GeFS( X) =, X = n 1 K n 1 b + B x 0 ( x,,x ) { 0, } n Fnd X { 0,1 } n that maxmzes GeFS(X) 39/50
40 Traffc features relevant for IDS Several feature selecton measures representable n the form (*) The Correlaton Feature Selecton (CFS) measure The mnmal Redundancy Maxmal Relevance (mrmr) measure Etc. 40/50
41 The CFS measure The mert functon of a feature subset S consstng of k features Mert S ( k) = k + k kr fc ( k 1) r ff where r fc s the average value of all featureclassfcaton correlatons and r ff s the average value of all feature feature correlatons 41/50
42 The CFS measure The mert functon reflects the followng ntutve hypothess about qualty of a feature subset Good feature subsets contan features hghly correlated wth the classfcaton, yet uncorrelated to each other The mert functon s maxmzed n the CFS measure max S { ( k), 1 k n} Mert S 42/50
43 The CFS measure It can be shown that the problem of maxmzaton of the mert functon can be presented as an nstance of the GeFS measure (GeFS CFS ) ( ) + = = n j j j n x x b x x a max X NISlab, Gjøvk Unversty College /50
44 The mrmr measure Based on mutual nformaton The relevance of features and the redundancy between features are consdered smultaneously 44/50
45 The mrmr measure The relevance of a feature set S for the class c 1 S ( ) = I( f,c) D S,c f S The redundancy between features n S ( S ) = I( f, f ) S 1 R 2 f, f j S j 45/50
46 The mrmr measure Combng the relevance and redundancy measures, we get the mrmr measure, whch s to be maxmzed max S 1 S f S I ( f,c) I ( f, f ) S 1 2 f, f j S j 46/50
47 The mrmr measure It can be shown that the problem of maxmzaton of the mrmr measure can also be presented as an nstance of the GeFS measure (GeFS mrmr ) ( ) = = = = max n n j, j j n n x x x a x x c X NISlab, Gjøvk Unversty College /50
48 Solvng the optmzaton problems The problems of maxmzng GeFS CFS and GeFS mrmr can be solved f we analyze them as problems of fractonal programmng In partcular, these problems pertan to the category of Polynomal Mxed 0 1 Fractonal Programmng problems (PM01FP) 48/50
49 Solvng the optmzaton problems The general form of PM01FP n m a + a j= j x 1 k J k mn = n 1 b + b j j x 1 k = k J under the followng constrants n b + b x,,,m j j k J k > 0 = 1 K = 1 n c p + c x, p,,m j pj k 0 = 1 K 1 x k = k J { 01, },k J a,b,c p,a j,b j, c pj R 49/50
50 Solvng the optmzaton problems By ntroducng approprate substtutons, such a PM01FP can be transformed nto a Mxed 0 1 Lnear Programmng Problem (M01LP) M01LP can be solved by means of the branch andbound method A globally optmal soluton s obtaned The number of varables and constrants n the M01LP s lnear n the number n of full set features 50/50
1 Approximation Algorithms
CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationQuestions that we may have about the variables
Antono Olmos, 01 Multple Regresson Problem: we want to determne the effect of Desre for control, Famly support, Number of frends, and Score on the BDI test on Perceved Support of Latno women. Dependent
More informationCS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements
Lecture 3 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there
More informationFace Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)
Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton
More informationLinear Regression, Regularization BiasVariance Tradeoff
HTF: Ch3, 7 B: Ch3 Lnear Regresson, Regularzaton BasVarance Tradeoff Thanks to C Guestrn, T Detterch, R Parr, N Ray 1 Outlne Lnear Regresson MLE = Least Squares! Bass functons Evaluatng Predctors Tranng
More informationStochastic Protocol Modeling for Anomaly Based Network Intrusion Detection
Stochastc Protocol Modelng for Anomaly Based Network Intruson Detecton Juan M. EstevezTapador, Pedro GarcaTeodoro, and Jesus E. DazVerdejo Department of Electroncs and Computer Technology Unversty of
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):18841889 Research Artcle ISSN : 09757384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More information1 Example 1: Axisaligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationStudy on Model of Risks Assessment of Standard Operation in Rural Power Network
Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,
More informationState function: eigenfunctions of hermitian operators> normalization, orthogonality completeness
Schroednger equaton Basc postulates of quantum mechancs. Operators: Hermtan operators, commutators State functon: egenfunctons of hermtan operators> normalzaton, orthogonalty completeness egenvalues and
More informationRELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT
Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE
More informationCommunication Networks II Contents
8 / 1  Communcaton Networs II (Görg)  www.comnets.unbremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationPerformance Analysis and Coding Strategy of ECOC SVMs
Internatonal Journal of Grd and Dstrbuted Computng Vol.7, No. (04), pp.6776 http://dx.do.org/0.457/jgdc.04.7..07 Performance Analyss and Codng Strategy of ECOC SVMs Zhgang Yan, and Yuanxuan Yang, School
More informationA GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION
A GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION Helena Vasconcelos INESC Porto hvasconcelos@nescportopt J N Fdalgo INESC Porto and FEUP jfdalgo@nescportopt
More informationDescriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications
CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary
More informationAn Analysis of Central Processor Scheduling in Multiprogrammed Computer Systems
STANCS73355 I SUSE73013 An Analyss of Central Processor Schedulng n Multprogrammed Computer Systems (Dgest Edton) by Thomas G. Prce October 1972 Techncal Report No. 57 Reproducton n whole or n part
More informationLogistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification
Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson
More informationAn MILP model for planning of batch plants operating in a campaignmode
An MILP model for plannng of batch plants operatng n a campagnmode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafeconcet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño
More informationConversion between the vector and raster data structures using Fuzzy Geographical Entities
Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationGibbs Free Energy and Chemical Equilibrium (or how to predict chemical reactions without doing experiments)
Gbbs Free Energy and Chemcal Equlbrum (or how to predct chemcal reactons wthout dong experments) OCN 623 Chemcal Oceanography Readng: Frst half of Chapter 3, Snoeynk and Jenkns (1980) Introducton We want
More informationEfficient Project Portfolio as a tool for Enterprise Risk Management
Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationNonlinear data mapping by neural networks
Nonlnear data mappng by neural networks R.P.W. Dun Delft Unversty of Technology, Netherlands Abstract A revew s gven of the use of neural networks for nonlnear mappng of hgh dmensonal data on lower dmensonal
More informationPOLYSA: A Polynomial Algorithm for Nonbinary Constraint Satisfaction Problems with and
POLYSA: A Polynomal Algorthm for Nonbnary Constrant Satsfacton Problems wth and Mguel A. Saldo, Federco Barber Dpto. Sstemas Informátcos y Computacón Unversdad Poltécnca de Valenca, Camno de Vera s/n
More information2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet
2008/8 An ntegrated model for warehouse and nventory plannng Géraldne Strack and Yves Pochet CORE Voe du Roman Pays 34 B1348 LouvanlaNeuve, Belgum. Tel (32 10) 47 43 04 Fax (32 10) 47 43 01 Emal: corestatlbrary@uclouvan.be
More informationSensitivity Analysis in a Generic MultiAttribute Decision Support System
Senstvty Analyss n a Generc MultAttrbute Decson Support System Sxto RíosInsua, Antono Jménez and Alfonso Mateos Department of Artfcal Intellgence, Madrd Techncal Unversty Campus de Montegancedo s/n,
More informationStudy on CET4 Marks in China s Graded English Teaching
Study on CET4 Marks n Chna s Graded Englsh Teachng CHE We College of Foregn Studes, Shandong Insttute of Busness and Technology, P.R.Chna, 264005 Abstract: Ths paper deploys Logt model, and decomposes
More informationMultivariate EWMA Control Chart
Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant
More informationAutomated Network Performance Management and Monitoring via Oneclass Support Vector Machine
Automated Network Performance Management and Montorng va Oneclass Support Vector Machne R. Zhang, J. Jang, and S. Zhang Dgtal Meda & Systems Research Insttute, Unversty of Bradford, UK Abstract: In ths
More informationMining Feature Importance: Applying Evolutionary Algorithms within a Webbased Educational System
Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Webbased Educatonal System Behrouz MINAEIBIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons
More informationPowerofTwo Policies for Single Warehouse MultiRetailer Inventory Systems with Order Frequency Discounts
Powerofwo Polces for Sngle Warehouse MultRetaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationGender Classification for RealTime Audience Analysis System
Gender Classfcaton for RealTme Audence Analyss System Vladmr Khryashchev, Lev Shmaglt, Andrey Shemyakov, Anton Lebedev Yaroslavl State Unversty Yaroslavl, Russa vhr@yandex.ru, shmaglt_lev@yahoo.com, andrey.shemakov@gmal.com,
More informationFORCED CONVECTION HEAT TRANSFER IN A DOUBLE PIPE HEAT EXCHANGER
FORCED CONVECION HEA RANSFER IN A DOUBLE PIPE HEA EXCHANGER Dr. J. Mchael Doster Department of Nuclear Engneerng Box 7909 North Carolna State Unversty Ralegh, NC 276957909 Introducton he convectve heat
More informationLecture 18: Clustering & classification
O CPS260/BGT204. Algorthms n Computatonal Bology October 30, 2003 Lecturer: Pana K. Agarwal Lecture 8: Clusterng & classfcaton Scrbe: Daun Hou Open Problem In HomeWor 2, problem 5 has an open problem whch
More informationCS 2750 Machine Learning. Lecture 17a. Clustering. CS 2750 Machine Learning. Clustering
Lecture 7a Clusterng Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square Clusterng Groups together smlar nstances n the data sample Basc clusterng problem: dstrbute data nto k dfferent groups such that
More informationSolving Factored MDPs with Continuous and Discrete Variables
Solvng Factored MPs wth Contnuous and screte Varables Carlos Guestrn Berkeley Research Center Intel Corporaton Mlos Hauskrecht epartment of Computer Scence Unversty of Pttsburgh Branslav Kveton Intellgent
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 6105194390,
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationOptimal Bidding Strategies for Generation Companies in a DayAhead Electricity Market with Risk Management Taken into Account
Amercan J. of Engneerng and Appled Scences (): 86, 009 ISSN 94700 009 Scence Publcatons Optmal Bddng Strateges for Generaton Companes n a DayAhead Electrcty Market wth Rsk Management Taken nto Account
More informationSingle and multiple stage classifiers implementing logistic discrimination
Sngle and multple stage classfers mplementng logstc dscrmnaton Hélo Radke Bttencourt 1 Dens Alter de Olvera Moraes 2 Vctor Haertel 2 1 Pontfíca Unversdade Católca do Ro Grande do Sul  PUCRS Av. Ipranga,
More informationImproved Mining of Software Complexity Data on Evolutionary Filtered Training Sets
Improved Mnng of Software Complexty Data on Evolutonary Fltered Tranng Sets VILI PODGORELEC Insttute of Informatcs, FERI Unversty of Marbor Smetanova ulca 17, SI2000 Marbor SLOVENIA vl.podgorelec@unmb.s
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationSIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA
SIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA E. LAGENDIJK Department of Appled Physcs, Delft Unversty of Technology Lorentzweg 1, 68 CJ, The Netherlands Emal: e.lagendjk@tnw.tudelft.nl
More informationCHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
More informationSearching for Interacting Features for Spam Filtering
Searchng for Interactng Features for Spam Flterng Chuanlang Chen 1, YunChao Gong 2, Rongfang Be 1,, and X. Z. Gao 3 1 Department of Computer Scence, Bejng Normal Unversty, Bejng 100875, Chna 2 Software
More informationOn the Optimal Control of a Cascade of HydroElectric Power Stations
On the Optmal Control of a Cascade of HydroElectrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More informationDynamic Scheduling of Emergency Department Resources
Dynamc Schedulng of Emergency Department Resources Junchao Xao Laboratory for Internet Software Technologes, Insttute of Software, Chnese Academy of Scences P.O.Box 8718, No. 4 South Fourth Street, Zhong
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
More informationRing structure of splines on triangulations
www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAMReport 201448 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n
More informationTime Series Analysis in Studies of AGN Variability. Bradley M. Peterson The Ohio State University
Tme Seres Analyss n Studes of AGN Varablty Bradley M. Peterson The Oho State Unversty 1 Lnear Correlaton Degree to whch two parameters are lnearly correlated can be expressed n terms of the lnear correlaton
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationFinancial market forecasting using a twostep kernel learning method for the support vector regression
Ann Oper Res (2010) 174: 103 120 DOI 10.1007/s1047900803577 Fnancal market forecastng usng a twostep kernel learnng method for the support vector regresson L Wang J Zhu Publshed onlne: 28 May 2008
More informationGenetic algorithm for searching for critical slip surface in gravity dams based on stress fields CHEN Jianyun 1, WANG Shu 2, XU Qiang 3, LI Jing 4
Advanced Materals Research Onlne: 2030904 ISSN: 6628985, Vol. 790, pp 4649 do:0.4028/www.scentfc.net/amr.790.46 203 Trans Tech Publcatons, Swtzerland Genetc algorthm for searchng for crtcal slp surface
More informationx f(x) 1 0.25 1 0.75 x 1 0 1 1 0.04 0.01 0.20 1 0.12 0.03 0.60
BIVARIATE DISTRIBUTIONS Let be a varable that assumes the values { 1,,..., n }. Then, a functon that epresses the relatve frequenc of these values s called a unvarate frequenc functon. It must be true
More informationLesson 2 Chapter Two Three Phase Uncontrolled Rectifier
Lesson 2 Chapter Two Three Phase Uncontrolled Rectfer. Operatng prncple of three phase half wave uncontrolled rectfer The half wave uncontrolled converter s the smplest of all three phase rectfer topologes.
More informationSoftware project management with GAs
Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de
More informationA Hierarchical Anomaly Network Intrusion Detection System using Neural Network Classification
IDC IDC A Herarchcal Anomaly Network Intruson Detecton System usng Neural Network Classfcaton ZHENG ZHANG, JUN LI, C. N. MANIKOPOULOS, JAY JORGENSON and JOSE UCLES ECE Department, New Jersey Inst. of Tech.,
More informationORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY TIME UNDER THE BACKGROUND OF BIG DATA
Internatonal Journal of Computer Scence and Applcatons, Technomathematcs Research Foundaton Vol. 13, No. 1, pp. 84 92, 2016 ORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY
More informationA Computer Technique for Solving LP Problems with Bounded Variables
Dhaka Unv. J. Sc. 60(2): 163168, 2012 (July) A Computer Technque for Solvng LP Problems wth Bounded Varables S. M. Atqur Rahman Chowdhury * and Sanwar Uddn Ahmad Department of Mathematcs; Unversty of
More informationImproved SVM in Cloud Computing Information Mining
Internatonal Journal of Grd Dstrbuton Computng Vol.8, No.1 (015), pp.3340 http://dx.do.org/10.1457/jgdc.015.8.1.04 Improved n Cloud Computng Informaton Mnng Lvshuhong (ZhengDe polytechnc college JangSu
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationHYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION
HYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION Abdul Ghapor Hussn Centre for Foundaton Studes n Scence Unversty of Malaya 563 KUALA LUMPUR Emal: ghapor@umedumy Abstract Ths paper
More informationEffective Network Defense Strategies against Malicious Attacks with Various Defense Mechanisms under Quality of Service Constraints
Effectve Network Defense Strateges aganst Malcous Attacks wth Varous Defense Mechansms under Qualty of Servce Constrants Frank YeongSung Ln Department of Informaton Natonal Tawan Unversty Tape, Tawan,
More informationInterpreting Patterns and Analysis of Acute Leukemia Gene Expression Data by Multivariate Statistical Analysis
Interpretng Patterns and Analyss of Acute Leukema Gene Expresson Data by Multvarate Statstcal Analyss ChangKyoo Yoo * and Peter A. Vanrolleghem BIOMATH, Department of Appled Mathematcs, Bometrcs and Process
More informationProduction. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.
Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s nonempty If Y s empty, we have nothng to talk about 2. Y s closed A set
More informationSVM Tutorial: Classification, Regression, and Ranking
SVM Tutoral: Classfcaton, Regresson, and Rankng Hwanjo Yu and Sungchul Km 1 Introducton Support Vector Machnes(SVMs) have been extensvely researched n the data mnng and machne learnng communtes for the
More informationVision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION
Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble
More informationSensor placement for leak detection and location in water distribution networks
Sensor placement for leak detecton and locaton n water dstrbuton networks ABSTRACT R. Sarrate*, J. Blesa, F. Near, J. Quevedo Automatc Control Department, Unverstat Poltècnca de Catalunya, Rambla de Sant
More informationChapter 7: Answers to Questions and Problems
19. Based on the nformaton contaned n Table 73 of the text, the food and apparel ndustres are most compettve and therefore probably represent the best match for the expertse of these managers. Chapter
More informationNegative Selection and Niching by an Artificial Immune System for Network Intrusion Detection
Negatve Selecton and Nchng by an Artfcal Immune System for Network Intruson Detecton Jungwon Km and Peter Bentley Department of omputer Scence, Unversty ollege London, Gower Street, London, W1E 6BT, U.K.
More informationFormulating & Solving Integer Problems Chapter 11 289
Formulatng & Solvng Integer Problems Chapter 11 289 The Optonal Stop TSP If we drop the requrement that every stop must be vsted, we then get the optonal stop TSP. Ths mght correspond to a ob sequencng
More informationActivity Scheduling for CostTime Investment Optimization in Project Management
PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta San Sebastán, September 8 th 10 th 010 Actvty Schedulng
More informationAvailabilityBased Path Selection and Network Vulnerability Assessment
AvalabltyBased Path Selecton and Network Vulnerablty Assessment Song Yang, Stojan Trajanovsk and Fernando A. Kupers Delft Unversty of Technology, The Netherlands {S.Yang, S.Trajanovsk, F.A.Kupers}@tudelft.nl
More informationI. SCOPE, APPLICABILITY AND PARAMETERS Scope
D Executve Board Annex 9 Page A/R ethodologcal Tool alculaton of the number of sample plots for measurements wthn A/R D project actvtes (Verson 0) I. SOPE, PIABIITY AD PARAETERS Scope. Ths tool s applcable
More informationMethodology to Determine Relationships between Performance Factors in Hadoop Cloud Computing Applications
Methodology to Determne Relatonshps between Performance Factors n Hadoop Cloud Computng Applcatons Lus Eduardo Bautsta Vllalpando 1,2, Alan Aprl 1 and Alan Abran 1 1 Department of Software Engneerng and
More informationOnLine Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
OnLne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com
More informationMETHODOLOGY TO DETERMINE RELATIONSHIPS BETWEEN PERFORMANCE FACTORS IN HADOOP CLOUD COMPUTING APPLICATIONS
METHODOLOGY TO DETERMINE RELATIONSHIPS BETWEEN PERFORMANCE FACTORS IN HADOOP CLOUD COMPUTING APPLICATIONS Lus Eduardo Bautsta Vllalpando 1,2, Alan Aprl 1 and Alan Abran 1 1 Department of Software Engneerng
More informationThe Network flow Motoring System based on Particle Swarm Optimized
The Network flow Motorng System based on Partcle Swarm Optmzed Neural Network Adult Educaton College, Hebe Unversty of Archtecture, Zhangjakou Hebe 075000, Chna Abstract The compatblty of the commercal
More informationProduct Quality and Safety Incident Information Tracking Based on Web
Product Qualty and Safety Incdent Informaton Trackng Based on Web News 1 Yuexang Yang, 2 Correspondng Author Yyang Wang, 2 Shan Yu, 2 Jng Q, 1 Hual Ca 1 Chna Natonal Insttute of Standardzaton, Beng 100088,
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationJoint Scheduling of Processing and Shuffle Phases in MapReduce Systems
Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, AlcatelLucent
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More informationAnts Can Schedule Software Projects
Ants Can Schedule Software Proects Broderck Crawford 1,2, Rcardo Soto 1,3, Frankln Johnson 4, and Erc Monfroy 5 1 Pontfca Unversdad Católca de Valparaíso, Chle FrstName.Name@ucv.cl 2 Unversdad Fns Terrae,
More informationPerformance Evaluation of MultiStage ChangePoint Detection Scheme against DDoS Attacks by Random Scan Worms
Performance Evaluaton of MultStage ChangePont Detecton Scheme aganst DDoS Attacks by Random Scan Worms Tutomu Murase *, Yuknobu Fukushma **, Masayosh Kobayash *, Sakko Nshmoto **, Ryohe Fumak * and Tokum
More information9.1 The Cumulative Sum Control Chart
Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationAN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE YuL Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
More informationChapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT
Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the
More informationNumber of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000
Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from
More information