Time Series Analysis of Network Traffic
|
|
|
- Percival Barrett
- 10 years ago
- Views:
Transcription
1 Time Series Analysis of Network Traffic Cyriac James IIT MADRAS February 9, 211 Cyriac James (IIT MADRAS) February 9, / 31
2 Outline of the presentation Background Motivation for the Work Network Trace Analysis and Results Conclusion Drawbacks and Future Work Cyriac James (IIT MADRAS) February 9, / 31
3 Background Traffic Analysis: Looking for Invariants Modeling and Prediction: Linear Models Features Assumptions: Stationarity Predictability Linear fit Cyriac James (IIT MADRAS) February 9, / 31
4 Motivation for the Work Network Traffic is bursty and non-poisson. (Ref: W. E. Leland et al, V. Paxson et al) Can contradict assumptions: Stationarity Linear fit Predictability Stationarity doesn t imply Predictability. Experiments: In the context of TCP SYN Flood attack Cyriac James (IIT MADRAS) February 9, / 31
5 25 Polling Interval = 1s Actual data Predicted data 25 Polling Interval = 1s Actual data Predicted data Data set Data set Polling Interval (a) Prediction Polling Interval (b) Prediction-2 Figure: Time Series and Prediction Cyriac James (IIT MADRAS) February 9, / 31
6 Polling Interval = 1s Predicted data Actual data 8 Attack Period Data set Polling Interval (a) Detection-1 12 Polling Interval = 1s Predicted data Actual data 1 8 Attack Period 6 Model Getting Adjusted to Attack Data set Polling Interval (b) Detection-2 Cyriac James (IIT MADRAS) February 9, / 31
7 4 3 2 Actual Vs Predicted : Gaussian White Noise Actual Predicted Magnitude of the process Sampling Interval Figure: Prediction of WGN Ideally: Stationary and Predictable Cyriac James (IIT MADRAS) February 9, / 31
8 Unanswered Questions Are the assumptions true? Quantify Predictability? Good Feature? Across networks at all times? How often model parameters need to re-estimated? Relation between Stability, Stationarity, ACF, Hurst Exponent etc. Currently lacking: A systematic approach Cyriac James (IIT MADRAS) February 9, / 31
9 Network Trace Figure: Tenet Network Architecture Traces collected using tcpdump. Link Bandwidth: 4Mbps Feature: SYN - SYN/ACK Data Set-1: 26 th July 21 to 3 th July 21 Data Set-2: 23 rd August 21 to 27 th August 21 Data Set-3: 2 th September 21 to 24 th September 21 Cyriac James (IIT MADRAS) February 9, / 31
10 35 Data Set 1,Monday 35 Data Set 1,Tuesday 3 3 Half open count Half open count Sampling Interval (Seconds) Sampling Interval (Seconds) 35 Data Set 1,Wednesday 35 Data Set 1,Thursday 3 3 Half open count Half open count Sampling Interval (Seconds) Sampling Interval (Seconds) Figure: Original Time Series Cyriac James (IIT MADRAS) February 9, / 31
11 Time Series Transformations Study on Time invariant feature - inconclusive. Transformation of Time Series Differencing and Averaging Cyriac James (IIT MADRAS) February 9, / 31
12 Half open count:first difference Data Set 1,Monday Sampling Interval (Seconds) Half open count:first difference Data Set 1,Tuesday Sampling Interval (Seconds) Half open count:first difference Data Set 1,Wednesday Sampling Interval (Seconds) Half open count:first difference Data Set 1,Thursday Sampling Interval (Seconds) Figure: Difference Time Series Cyriac James (IIT MADRAS) February 9, / 31
13 35 Data Set 1,Monday 35 Data Set 1,Tuesday Average half open count Sampling Interval (Seconds) Average half open count Sampling Interval (Seconds) 35 Data Set 1,Wednesday 35 Data Set 1,Thursday Average half open count Average half open count Sampling Interval (Seconds) Sampling Interval (Seconds) Figure: Average Time Series Cyriac James (IIT MADRAS) February 9, / 31
14 Analysis and Results Cyriac James (IIT MADRAS) February 9, / 31
15 Stationarity Check First and second order moments should be invariant of time Day Data Set-1 Dat Set-2 Data Set-3 Monday Tuesday Wednesday Thursday Friday Average (a) Mean: Original Series Day Data Set-1 Dat Set-2 Data Set-3 Monday Tuesday Wednesday Thursday Friday Average (c) Mean: Average Series Day Data Set-1 Dat Set-2 Data Set-3 Monday Tuesday Wednesday Thursday Friday Average (b) Mean: Difference Series Cyriac James (IIT MADRAS) February 9, / 31
16 Stationarity and Auto-Correlation Function(ACF) Studies define stationarity in terms of ACF (Ref: H. Liu and M. S. Kim, G. Kirchgassner and J. Wolters) Fast decaying ACF > Stationary Slow decaying ACF > Non-Stationary Remember: All time series are found non-stationary. Cyriac James (IIT MADRAS) February 9, / 31
17 Data Set 1, Ensemble Average Lag Data Set 2, Ensemble Average Lag Data Set 3, Ensemble Average Lag ACF Plot: Original Series ACF ACF ACF Cyriac James (IIT MADRAS) February 9, / 31
18 Data Set 1, Ensemble Average Lag Data Set 2, Ensemble Average Lag Data Set 3, Ensemble Average Lag ACF Plot: Difference Series ACF ACF ACF Cyriac James (IIT MADRAS) February 9, / 31
19 Data Set 1, Ensemble Average Lag Data Set 2, Ensemble Average Lag Data Set 3, Ensemble Average Lag ACF Plot: Average Series ACF ACF ACF Cyriac James (IIT MADRAS) February 9, / 31
20 Stationarity and Stability Definition: Stability > Stationarity (Ref: G. Kirchgassner and J. Wolters) Consider the series as Linear Prediction (LP) process a t = α 1 a t 1 +α 2 a t 2 +ǫ t (1) where a t,a t 1,... is the time series data, α 1 and α 2 are the model coefficients and ǫ t is the random shock or residual at time t. Characteristic Equation: Yule-Walker Estimation: α 1 and α 2 x 2 α 1 x α 2 = (2) Table: Roots of Original Series Data Set Magnitude of the Root Cyriac James (IIT MADRAS) February 9, / 31
21 Smoothness Factor Matthew Roughan et al in their work on modeling backbone traffic have quantified the smoothness of the time series. Relative Variance (variance divided by the mean) Lower RV implies smoother series Table: Smoothness Data Set Original Series Difference Series Average Series Cyriac James (IIT MADRAS) February 9, / 31
22 Hurst Exponent Estimation Measure of the burstiness H =.5, is a white gaussian noise < H <.5, is a mean reverting and less bursty series.5 < H < 1, is a bursty and trend reinforcing series Rescaled Range Estimator used Table: Hurst exponent Data Set Original Series Difference Series Average Series Cyriac James (IIT MADRAS) February 9, / 31
23 Modeling and Prediction LP model with order 2 Parameter Estimation: Yule-Walker Method Training data: Monday - Thursday Testing: Friday Table: Average Relative Error Data Set Original Series Difference Series Average Series Cyriac James (IIT MADRAS) February 9, / 31
24 Modeling and Prediction Actual Vs Predicted : Data Set 1 Actual Predicted 14 Half open count Sampling Interval ( in seconds) 2 Actual vs Predicted : Data Set Actual Predicted Half open count : First difference Sampling Interval (in seconds) Average half open count Actual Vs Predicted : Data Set 1 Actual Predicted Sampling Interval ( in seconds) Cyriac James (IIT MADRAS) February 9, / 31
25 Detection Probability of False Negative (FN) Probability of False Positive (FP) Probability Threshold Value of Zero FN and 3% FP Threshold Figure: FP vs FN Cyriac James (IIT MADRAS) February 9, / 31
26 Detection 35 Prediction Error During an Attack: Data Set 1 3 Prediction Error Attack Period Sampling Interval (in seconds) Figure: FP vs FN Cyriac James (IIT MADRAS) February 9, / 31
27 Conclusion Assumption of stationarity is not correct in all cases Stability does not imply stationarity. ACF graph alone cannot conclude stationarity. Transformations appear promising. Predictable Series : Slowly decaying ACF, low Hurst exponent and low Relative variance. Window over which series is stationary? Cyriac James (IIT MADRAS) February 9, / 31
28 Drawbacks and Future Work Statistical Significance tests Other transformations: Median smoothening and Mean differencing. Hour based Analysis. Repeat experiments with traffic traces from a different source or network. Compare the traffic characteristics at the edge and core network. Application: Anomaly detection, Bandwidth management. Cyriac James (IIT MADRAS) February 9, / 31
29 Publications 1 Cyriac James and Hema A. Murthy, Time Series Analysis of Network Data: A Case Study, in Third International Workshop on Network Science for Communication Networks, In Conjuction with IEEE Infocom 211 Status: Submitted on 15th January 211 Cyriac James (IIT MADRAS) February 9, / 31
30 References G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control. Pearson Education, D. M. Divakaran, H. A. Murthy, and T. A. Gonsalves, Detection of SYN flooding attacks using linear prediction analysis, 14th IEEE International Conference on Networks, pp. 16, September 26. G. Zhang, S. Jiang, G. Wei, and Q. Guan, A prediction-based detection algorithm against distributed denial-of-service attacks, in Proceedings of the International Conference on Wireless Communications and Mobile Computing (IWCMC), June 29. J. Cheng, J. Yin, C. Wu, B. Zhang, and Y. Liu, DDOS attack detection method based on linear prediction model, in ICIC, 29. W. U. Qing-tao and S. Zhi-qing, Detecting DD O S attacks against web server using time series analysis, Wuhan Univesity Journal of Natural Sciences, vol. 11, no. 1, pp , 26. W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, On the self-similar nature of ethernet traffic, Cyriac James (IIT MADRAS) February 9, / 31
31 References V. Paxson and S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling, T. Karagiannis, M. Molle, and M. Faloutsos, Long-range dependence ten years of internet traffic modeling, in IEEE INTERNET COMPUT- ING, vol. 8, Sept 24, pp M. Roughan and J. Gottlieb, Large Scale Measurement and Modeling of Backbone Interent Traffic, in Internet Performance and Control of Network Systems, 22. B. Qian and K. Rasheed, Hurst Exponent And Financial Market Predictability, in IASTED conference on Financial Engineering and Applications, 24. H. Liu and M. S. Kim, Real-time detection of stealthy DD O S attacks using time-series decomposition, in Proceedings of ICC, July 21. G. Kirchgassner and J. Wolters, Introduction to Modern Time Series Analysis. Springer, 27. Cyriac James (IIT MADRAS) February 9, / 31
Observingtheeffectof TCP congestion controlon networktraffic
Observingtheeffectof TCP congestion controlon networktraffic YongminChoi 1 andjohna.silvester ElectricalEngineering-SystemsDept. UniversityofSouthernCalifornia LosAngeles,CA90089-2565 {yongminc,silvester}@usc.edu
Examining Self-Similarity Network Traffic intervals
Examining Self-Similarity Network Traffic intervals Hengky Susanto Byung-Guk Kim Computer Science Department University of Massachusetts at Lowell {hsusanto, kim}@cs.uml.edu Abstract Many studies have
Network Traffic Modeling and Prediction with ARIMA/GARCH
Network Traffic Modeling and Prediction with ARIMA/GARCH Bo Zhou, Dan He, Zhili Sun and Wee Hock Ng Centre for Communication System Research University of Surrey Guildford, Surrey United Kingdom +44(0)
MULTI-LEVEL NETWORK RESILIENCE: TRAFFIC ANALYSIS, ANOMALY DETECTION AND SIMULATION
ISSN: 2229-6948(ONLINE) ICTACT JOURNAL ON COMMUNICATION TECHNOLOGY: SPECIAL ISSUE ON NEXT GENERATION WIRELESS NETWORKS AND APPLICATIONS, JUNE 2011, VOLUME 2, ISSUE 2 MULTI-LEVEL NETWORK RESILIENCE: TRAFFIC
RESEARCH OF THE NETWORK SERVER IN SELF-SIMILAR TRAFFIC ENVIRONMENT
RESEARCH OF THE NETWORK SERVER IN SELF-SIMILAR TRAFFIC ENVIRONMENT Sergejs Ilnickis Keywords: network traffic, self-similar traffic, traffic analysis. Abstract - Last scientific publication shows that
IBM SPSS Forecasting 22
IBM SPSS Forecasting 22 Note Before using this information and the product it supports, read the information in Notices on page 33. Product Information This edition applies to version 22, release 0, modification
Time Series - ARIMA Models. Instructor: G. William Schwert
APS 425 Fall 25 Time Series : ARIMA Models Instructor: G. William Schwert 585-275-247 [email protected] Topics Typical time series plot Pattern recognition in auto and partial autocorrelations
2.2 Elimination of Trend and Seasonality
26 CHAPTER 2. TREND AND SEASONAL COMPONENTS 2.2 Elimination of Trend and Seasonality Here we assume that the TS model is additive and there exist both trend and seasonal components, that is X t = m t +
ON THE FRACTAL CHARACTERISTICS OF NETWORK TRAFFIC AND ITS UTILIZATION IN COVERT COMMUNICATIONS
ON THE FRACTAL CHARACTERISTICS OF NETWORK TRAFFIC AND ITS UTILIZATION IN COVERT COMMUNICATIONS Rashiq R. Marie Department of Computer Science email: [email protected] Helmut E. Bez Department of Computer
Promotional Forecast Demonstration
Exhibit 2: Promotional Forecast Demonstration Consider the problem of forecasting for a proposed promotion that will start in December 1997 and continues beyond the forecast horizon. Assume that the promotion
Internet Traffic Variability (Long Range Dependency Effects) Dheeraj Reddy CS8803 Fall 2003
Internet Traffic Variability (Long Range Dependency Effects) Dheeraj Reddy CS8803 Fall 2003 Self-similarity and its evolution in Computer Network Measurements Prior models used Poisson-like models Origins
Studying Achievement
Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us
Time Series Analysis
Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos
TIME SERIES ANALYSIS
TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 [email protected]. Introduction Time series (TS) data refers to observations
MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal
MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims
TIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi- 110 012 [email protected] 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
IBM SPSS Forecasting 21
IBM SPSS Forecasting 21 Note: Before using this information and the product it supports, read the general information under Notices on p. 107. This edition applies to IBM SPSS Statistics 21 and to all
ANALYZING NETWORK TRAFFIC FOR MALICIOUS ACTIVITY
CANADIAN APPLIED MATHEMATICS QUARTERLY Volume 12, Number 4, Winter 2004 ANALYZING NETWORK TRAFFIC FOR MALICIOUS ACTIVITY SURREY KIM, 1 SONG LI, 2 HONGWEI LONG 3 AND RANDALL PYKE Based on work carried out
Time Series Analysis
JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),
A TWO LEVEL ARCHITECTURE USING CONSENSUS METHOD FOR GLOBAL DECISION MAKING AGAINST DDoS ATTACKS
ICTACT JOURNAL ON COMMUNICATION TECHNOLOGY, JUNE 2010, ISSUE: 02 A TWO LEVEL ARCHITECTURE USING CONSENSUS METHOD FOR GLOBAL DECISION MAKING AGAINST DDoS ATTACKS S.Seetha 1 and P.Raviraj 2 Department of
Univariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
Characteristics of Network Traffic Flow Anomalies
Characteristics of Network Traffic Flow Anomalies Paul Barford and David Plonka I. INTRODUCTION One of the primary tasks of network administrators is monitoring routers and switches for anomalous traffic
4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression
Statistical analysis of Snort alarms for a medium-sized network
Statistical analysis of Snort alarms for a medium-sized network Kitti Chantawut [[email protected]], Bogdan Ghita [[email protected]] Centre for Security, Communications, and Network Research University
Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market
Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market Sergio Da Silva 1, Raul Matsushita 2, Iram Gleria 3, Annibal Figueiredo 4 1 Department of Economics, Federal University
Forecasting in supply chains
1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the
Time series Forecasting using Holt-Winters Exponential Smoothing
Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract
Network Traffic Characterization using Energy TF Distributions
Network Traffic Characterization using Energy TF Distributions Angelos K. Marnerides [email protected] Collaborators: David Hutchison - Lancaster University Dimitrios P. Pezaros - University
Stability of QOS. Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu
Stability of QOS Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu Abstract Given a choice between two services, rest of the things being equal, it is natural to prefer the one with more
Analysis of algorithms of time series analysis for forecasting sales
SAINT-PETERSBURG STATE UNIVERSITY Mathematics & Mechanics Faculty Chair of Analytical Information Systems Garipov Emil Analysis of algorithms of time series analysis for forecasting sales Course Work Scientific
Application Layer Traffic Analysis of a Peer-to-Peer System
Application Layer Traffic Analysis of a Peer-to-Peer System Dietmar Tutsch Technical University Berlin Institute of Computer Engineering and Microelectronics Berlin, Germany [email protected] Gilbert
Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London)
Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London) 1 Forecasting: definition Forecasting is the process of making statements about events whose
A Novel Distributed Denial of Service (DDoS) Attacks Discriminating Detection in Flash Crowds
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 9, December 2014, PP 139-143 ISSN 2349-4751 (Print) & ISSN 2349-476X (Online) A Novel Distributed Denial
Time Series Analysis and Forecasting
Time Series Analysis and Forecasting Math 667 Al Nosedal Department of Mathematics Indiana University of Pennsylvania Time Series Analysis and Forecasting p. 1/11 Introduction Many decision-making applications
Denial of Service and Anomaly Detection
Denial of Service and Anomaly Detection Vasilios A. Siris Institute of Computer Science (ICS) FORTH, Crete, Greece [email protected] SCAMPI BoF, Zagreb, May 21 2002 Overview! What the problem is and
Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model
Tropical Agricultural Research Vol. 24 (): 2-3 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute
Time Series Analysis: Basic Forecasting.
Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD [email protected]
Threshold Autoregressive Models in Finance: A Comparative Approach
University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Informatics 2011 Threshold Autoregressive Models in Finance: A Comparative
modeling Network Traffic
Aalborg Universitet Characterization and Modeling of Network Shawky, Ahmed Sherif Mahmoud; Bergheim, Hans ; Ragnarsson, Olafur ; Wranty, Andrzej ; Pedersen, Jens Myrup Published in: Proceedings of 6th
An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks
2011 International Conference on Network and Electronics Engineering IPCSIT vol.11 (2011) (2011) IACSIT Press, Singapore An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks Reyhaneh
Chapter 1. Vector autoregressions. 1.1 VARs and the identi cation problem
Chapter Vector autoregressions We begin by taking a look at the data of macroeconomics. A way to summarize the dynamics of macroeconomic data is to make use of vector autoregressions. VAR models have become
MODELING OF SYN FLOODING ATTACKS Simona Ramanauskaitė Šiauliai University Tel. +370 61437184, e-mail: [email protected]
MODELING OF SYN FLOODING ATTACKS Simona Ramanauskaitė Šiauliai University Tel. +370 61437184, e-mail: [email protected] A great proportion of essential services are moving into internet space making the
NOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS
NOVEL PRIORITISED EGPRS MEDIUM ACCESS REGIME FOR REDUCED FILE TRANSFER DELAY DURING CONGESTED PERIODS D. Todinca, P. Perry and J. Murphy Dublin City University, Ireland ABSTRACT The goal of this paper
Knowledge Based System for Detection and Prevention of DDoS Attacks using Fuzzy logic
Knowledge Based System for Detection and Prevention of DDoS Attacks using Fuzzy logic Amit Khajuria 1, Roshan Srivastava 2 1 M. Tech Scholar, Computer Science Engineering, Lovely Professional University,
Sales forecasting # 2
Sales forecasting # 2 Arthur Charpentier [email protected] 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting
Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents
Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents Prasanna Desikan and Jaideep Srivastava Department of Computer Science University of Minnesota. @cs.umn.edu
5. Multiple regression
5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful
I. Basic concepts: Buoyancy and Elasticity II. Estimating Tax Elasticity III. From Mechanical Projection to Forecast
Elements of Revenue Forecasting II: the Elasticity Approach and Projections of Revenue Components Fiscal Analysis and Forecasting Workshop Bangkok, Thailand June 16 27, 2014 Joshua Greene Consultant IMF-TAOLAM
Network Bandwidth Utilization Forecast Model on High Bandwidth Networks
Network Bandwidth Utilization Forecast Model on High Bandwidth Networks Wucherl Yoo and Alex Sim Lawrence Berkeley National Laboratory, Email: {wyoo,asim}@lbl.gov Abstract With the increasing number of
Univariate Time Series Analysis; ARIMA Models
Econometrics 2 Spring 25 Univariate Time Series Analysis; ARIMA Models Heino Bohn Nielsen of4 Outline of the Lecture () Introduction to univariate time series analysis. (2) Stationarity. (3) Characterizing
Maximizing the number of users in an interactive video-ondemand. Citation Ieee Transactions On Broadcasting, 2002, v. 48 n. 4, p.
Title Maximizing the number of users in an interactive video-ondemand system Author(s) Bakiras, S; Li, VOK Citation Ieee Transactions On Broadcasting, 2002, v. 48 n. 4, p. 281-292 Issued Date 2002 URL
Conclusions and Future Directions
Chapter 9 This chapter summarizes the thesis with discussion of (a) the findings and the contributions to the state-of-the-art in the disciplines covered by this work, and (b) future work, those directions
Accelerated Simulation Method for Power-law Traffic and Non- FIFO Scheduling
Accelerated Simulation Method for Power-law Traffic and Non- FIF Scheduling Authors: Sharifah H. S. Ariffin and John A. Schormans Department of Electronic Engineering, Queen Mary, University of London,
Signal Processing Methods for Denial of Service Attack Detection
0 Signal Processing Methods for Denial of Service Attack Detection Urbashi Mitra Ming Hsieh Department of Electrical Engineering Viterbi School of Engineering University of Southern California Los Angeles,
FINAL EXAM SECTIONS AND OBJECTIVES FOR COLLEGE ALGEBRA
FINAL EXAM SECTIONS AND OBJECTIVES FOR COLLEGE ALGEBRA 1.1 Solve linear equations and equations that lead to linear equations. a) Solve the equation: 1 (x + 5) 4 = 1 (2x 1) 2 3 b) Solve the equation: 3x
Load Balancing and Switch Scheduling
EE384Y Project Final Report Load Balancing and Switch Scheduling Xiangheng Liu Department of Electrical Engineering Stanford University, Stanford CA 94305 Email: [email protected] Abstract Load
Connection-level Analysis and Modeling of Network Traffic
ACM SIGCOMM INTERNET MEASUREMENT WORKSHOP Connection-level Analysis and Modeling of Network Traffic Shriram Sarvotham, Rudolf Riedi, Richard Baraniuk Abstract Most network traffic analysis and modeling
Effect of sampling rate and monitoring granularity on anomaly detectability
Effect of sampling rate and monitoring granularity on anomaly detectability Keisuke Ishibashi, Ryoichi Kawahara, Mori Tatsuya, Tsuyoshi Kondoh and Shoichiro Asano Information Sharing Platform Labs. NTT
A Compound Model for TCP Connection Arrivals
A Compound Model for TCP Connection Arrivals Carl J Nuzman Dept of Electrical Engineering Princeton University Iraj Saniee Wim Sweldens Alan Weiss Bell Laboratories, Lucent Technologies Murray Hill, NJ
Monitoring Trends in Network Flow for Situational Awareness
Monitoring Trends in Network Flow for Situational Awareness SEI CERT NetSA 2011 Carnegie Mellon University NO WARRANTY THIS MATERIAL OF CARNEGIE MELLON UNIVERSITY AND ITS SOFTWARE ENGINEERING INSTITUTE
Rob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1
Rob J Hyndman Forecasting using 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Outline 1 Regression with ARIMA errors 2 Example: Japanese cars 3 Using Fourier terms for seasonality 4
Chapter 9: Univariate Time Series Analysis
Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of
MONITORING OF TRAFFIC OVER THE VICTIM UNDER TCP SYN FLOOD IN A LAN
MONITORING OF TRAFFIC OVER THE VICTIM UNDER TCP SYN FLOOD IN A LAN Kanika 1, Renuka Goyal 2, Gurmeet Kaur 3 1 M.Tech Scholar, Computer Science and Technology, Central University of Punjab, Punjab, India
Forecasting methods applied to engineering management
Forecasting methods applied to engineering management Áron Szász-Gábor Abstract. This paper presents arguments for the usefulness of a simple forecasting application package for sustaining operational
Network Performance Measurement and Analysis
Network Performance Measurement and Analysis Outline Measurement Tools and Techniques Workload generation Analysis Basic statistics Queuing models Simulation CS 640 1 Measurement and Analysis Overview
An Efficient Filter for Denial-of-Service Bandwidth Attacks
An Efficient Filter for Denial-of-Service Bandwidth Attacks Samuel Abdelsayed, David Glimsholt, Christopher Leckie, Simon Ryan and Samer Shami Department of Electrical and Electronic Engineering ARC Special
Round-Trip Time Inference Via Passive Monitoring
Round-Trip Time Inference Via Passive Monitoring Ryan Lance Department of Mathematics University of Maryland College Park, Maryland [email protected] Ian Frommer Applied Mathematics and Scientific Computation
PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard
Some useful concepts in univariate time series analysis
Some useful concepts in univariate time series analysis Autoregressive moving average models Autocorrelation functions Model Estimation Diagnostic measure Model selection Forecasting Assumptions: 1. Non-seasonal
Bandwidth Allocation DBA (BA-DBA) Algorithm for xpon Networks
Bandwidth Allocation DBA (BA-DBA) Algorithm for xpon Networks Karamitsos Ioannis Telecom Lab,On Telecoms Athens, Attica 16342, Greece Al-Arfaj Khalid Rayadah Investment Co Riyadh, KSA Kingdom of Saudi
Internet Activity Analysis Through Proxy Log
Internet Activity Analysis Through Proxy Log Kartik Bommepally, Glisa T. K., Jeena J. Prakash, Sanasam Ranbir Singh and Hema A Murthy Department of Computer Science Indian Institute of Technology Madras,
Graphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models
Graphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models William Q. Meeker Department of Statistics Iowa State University Ames, IA 50011 January 13, 2001 Abstract S-plus is a highly
Introduction to Longitudinal Data Analysis
Introduction to Longitudinal Data Analysis Longitudinal Data Analysis Workshop Section 1 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 1: Introduction