DOCSLIB.ORG

An Empirical Analysis of Network Traffic: Device Profiling and Classification

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Empirical Analysis of Network Traffic: Device Profiling and Classification University of Massachusetts Amherst ScholarWorks@UMass Amherst Masters Theses Dissertations and Theses July 2019 An Empirical Analysis of Network Traffic: viceDe Profiling and Classification Mythili Vishalini Anbazhagan University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/masters_theses_2 Recommended Citation Anbazhagan, Mythili Vishalini, "An Empirical Analysis of Network Traffic: viceDe Profiling and Classification" (2019). Masters Theses. 756. https://doi.org/10.7275/6sh3-za20 https://scholarworks.umass.edu/masters_theses_2/756 This Open Access Thesis is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Masters Theses by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. AN EMPIRICAL ANALYSIS OF NETWORK TRAFFIC: DEVICE PROFILING AND CLASSIFICATION A Thesis Presented by MYTHILI VISHALINI ANBAZHAGAN Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 2019 Department of Electrical and Computer Engineering AN EMPIRICAL ANALYSIS OF NETWORK TRAFFIC: DEVICE PROFILING AND CLASSIFICATION A Thesis Presented by MYTHILI VISHALINI ANBAZHAGAN Approved as to style and content by: David Irwin, Chair Daniel Holcomb, Member Michael Zink, Member Robert Jackson, Department Head Department of Electrical and Computer Engi- neering ABSTRACT AN EMPIRICAL ANALYSIS OF NETWORK TRAFFIC: DEVICE PROFILING AND CLASSIFICATION MAY 2019 MYTHILI VISHALINI ANBAZHAGAN B.E., KUMARAGURU COLLEGE OF TECHNOLOGY MSECE, UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor David Irwin Time and again we have seen the Internet grow and evolve at an unprecedented scale. The number of online users in 1995 was 40 million but in 2020, number of online devices are predicted to reach 50 billion, which would be 7 times the human population on earth. Up until now, the revolution was in the digital world. But now, the revolution is happening in the physical world that we live in; IoT devices are employed in all sorts of environments like domestic houses, hospitals, industrial spaces, nuclear plants etc., Since they are employed in a lot of mission-critical or even life-critical environments, their security and reliability are of paramount importance because compromising them can lead to grave consequences. IoT devices are, by nature, different from conventional Internet connected devices like laptops, smart phones etc., They have small memory, limited storage, low pro- cessing power etc., They also operate with little to no human intervention. Hence it becomes very important to understand IoT devices better. How do they behave iii in a network? How different are they from traditional Internet connected devices? Can they be identified from their network traffic? Is it possible for anyone to identify them just by looking at the network data that leaks outside the network, without even joining the network? That is the aim of this thesis. To the best of our knowledge, no study has collected data from outside the network, without joining the network, with the intention of finding out if IoT devices can be identified from this data. We also identify parameters that classify IoT and non-IoT devices. Then we do manual grouping of similar devices and then do the grouping automatically, using clustering algorithms. This will help in grouping devices of similar nature and create a profile for each kind of device. iv TABLE OF CONTENTS Page ABSTRACT .......................................................... iii LIST OF TABLES ...................................................viii LIST OF FIGURES................................................... xi CHAPTER 1. INTRODUCTION ................................................. 1 2. MOTIVATION .................................................... 3 2.1 Privacy Risks in IoT . .4 2.2 Security Risks in IoT . .5 3. RELATED WORK ............................................... 10 4. DESIGN AND IMPLEMENTATION ............................. 13 4.1 Capturing the Network Data . 13 4.2 Points of Traffic Observation . 13 4.3 Modes of Operation of the NIC . 14 4.4 Places and Number of Traffic Captures . 15 5. ANALYSIS OF DATA CAPTURED FROM OUTSIDE THE NETWORK ................................................... 17 5.1 Data Encapsulation . 17 5.2 Identifying the various Networks present in the vicinity and their degree of proximity . 19 5.3 Identifying the Devices belonging to a Network . 21 5.4 Identifying the Type of Device and Vendor using OUI . 23 5.5 Degree of Activeness of a Device . 26 5.6 How often do Devices change states ? . 32 5.7 Traffic associated with a device . 39 v 5.7.1 Content Type of Packets associated with a Device . 39 5.7.2 How much Traffic do Devices Send and Receive ? . 45 5.7.3 Do Devices consistently send the same amount of traffic everyday? . 51 5.7.4 Do Devices only send data or receive data or both ? . 56 5.8 Conclusion . 61 6. ANALYSIS OF DATA CAPTURED FROM INSIDE THE NETWORK ................................................... 62 6.1 Data Encapsulation . 62 6.2 Network Protocols used by a Device . 64 6.2.1 Wired Medium. 65 6.2.2 Wireless Medium . 70 6.3 Network Ports used for communication . 73 6.3.1 Wired Medium. 75 6.3.2 Wireless Medium . 79 6.4 How many external servers do devices contact each day? . 81 6.4.1 Wired Medium. 82 6.4.2 Wireless Medium . 86 6.5 Destination Devices Contacted within the network . 86 6.5.1 Wired Medium. 87 6.5.2 Wireless Medium . 88 6.6 Traffic Associated with a device . 89 6.6.1 How much Traffic do Devices Send and Receive ? . 90 6.6.1.1 Wired Medium . 90 6.6.1.2 Wireless Medium. 92 6.6.2 Do Devices consistently send the same amount of traffic everyday? . 95 6.6.2.1 Wired Interface . 95 6.6.2.2 Wireless Interface . 97 6.7 Do Devices only send data or receive data or both ? . 99 vi 6.7.1 Wired Interface . 100 6.7.2 Wireless Interface . 101 6.8 Conclusion . 102 7. CLUSTERING .................................................. 103 7.1 Defining Network Signature . 104 7.1.1 Quantifying Signature Strength . 105 7.1.2 Identifying Parameters Unique to a device . 105 7.2 Input Data to the Clustering Algorithm . 106 7.2.1 External Wireless . 106 7.2.2 Internal Wired and Wireless . 106 7.3 Cluster Progression - External vs. Internal . 107 7.4 Internal Wired . 109 7.5 Internal Wireless . 111 7.6 External Wireless . 113 7.7 External vs. Internal clustering . 116 7.8 Conclusion . 119 8. FUTURE WORK ................................................ 120 BIBLIOGRAPHY .................................................. 122 vii LIST OF TABLES Table Page 5.1 A subset of fields present in the IEEE 802.11 Header . 18 5.2 Location A - List of Networks . 19 5.3 Location B - List of Networks . 22 5.4 IEEE 802.11 - Values in Address Fields based on the DS Value . 23 5.5 Example List of OUIs and Assignees . 24 5.6 Location A - Subset of List of devices after OUI resolution . 25 5.7 Location A - Network 'HostNetwork' - Comparison of One Day's Duration of Sleep and Active States of an Apple TV, Nest Thermostat and Access Point . 27 5.8 Location A - Network 'HostNetworkGuest' - Comparison of One Day's Duration of Sleep and Active States of devices Apple 4a:aa:13, Apple 3e:39:83 and Access Point . 28 5.9 Location A - Network 'HostNetwork' - Inference - Mean of Number of Transitions per hour and their fluctuation 120+ days . 35 5.10 Location A - Network 'HostNetworkGuest' - Inference - Mean of Number of Transitions per hour and their fluctuation over 120+ days ........................................................ 36 5.11 Location A - Network 'HostNetwork' - Average Percentage of Types of Packets transmitted and received in a day . 41 5.12 Location A - Network 'HostNetworkGuest' - Average Percentage of Types of Packets transmitted and received in a day . 41 5.13 Location A - Other Networks - Average Percentage of Types of Packets transmitted and received in a day . 44 viii 5.14 Traffic Volume Limits for Traffic Categorization . 45 5.15 Location A - Network 'HostNetwork' - Mean of Average Traffic generated in a minute over all days . 47 5.16 Location A - Network HostNetworkGuest - Mean of Average Traffic generated in a minute over all days . 48 5.17 Location A - Outside Networks - Mean of Average Traffic generated in a minute over all days . 50 5.18 Standard Deviation Limits for Traffic Fluctuation Category . 52 5.19 Location A - Network 'HostNetwork' - Fluctuations in Traffic over all days ........................................................ 53 5.20 Location A - Network 'HostNetworkGuest' - Fluctuations in Traffic over all days . ..
Load more

Recommended publications
  • Personalized Smart Residency with Human Activity Recognition a Thesis Submitted to the Graduate School in Partial Fulfillment Of
    PERSONALIZED SMART RESIDENCY WITH HUMAN ACTIVITY RECOGNITION A THESIS SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE MASTER IN COMPUTER SCIENCE BY SUDAD H. ABED DR. WU SHAOEN – ADVISOR BALL STATE UNIVERSITY MUNCIE, INDIANA December 2016 DEDICATION To our greatest teacher, the person who took us out of the darkness to the light, the person who carried discomfort on his shoulders for our comfort, our prophet Mohammad; To the pulse of my heart, the secure and warm lap, the one who stayed up to ensure my wellness, my mother; To the man who devoted his life to me, my first friend, the one who I will always go to for advice, my father; To those whom I stood up miss every day, the companions who have stood by me through the good times and the bad, my brothers and sisters; I dedicate my work to you. i ACKNOWLEDGMENT First of all, and most importantly, I thank Allah for all the strength, blessings, and mercy he has been given me. I want to pay a special warm thanks to my teacher and thesis advisor Dr. Shaoen Wu for his guidance, answers, and patience every time I needed him during my work on the thesis. I want to acknowledge my appreciation to Dr. Shaoen Wu for all his effort and support in helping me complete this work. I want to thank my committee members, Dr. David Hua and Dr. Jeff Zhang for all their suggestions and recommendations. Also, I want to thank the chairperson of the Computer Science Department, Dr.
    [Show full text]
  • Cloud Computingcomputing
    NetworkingNetworking TechnologiesTechnologies forfor BigBig DataData andand InternetInternet ofof ThingsThings. Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 [email protected] These slides and audio/video recordings of this lecture are at: http://www.cse.wustl.edu/~jain/tutorials/gitma15.htm Washington University in St. Louis http://www.cse.wustl.edu/~jain/tutorials/gitma15.htm ©2015 Raj Jain 10-1 OverviewOverview 1. What are Things? 2. What’s Smart? 3. Why IoT Now? 4. Business/Research Opportunities in IoT 5. Why, What, and How of Big Data: It’s all because of advances in networking 6. Recent Developments in Networking and their role in Big Data (Virtualization, SDN, NFV) Washington University in St. Louis http://www.cse.wustl.edu/~jain/tutorials/gitma15.htm ©2015 Raj Jain 10-2 CloudCloud ComputingComputing August 25, 2006: Amazon announced EC2 Birth of Cloud Computing in reality (Prior theoretical concepts of computing as a utility) Web Services To Drive Future Growth For Amazon ($2B in 2012, $7B in 2019) - Forbes, Aug 12, 2012 Cloud computing was made possible by computing virtualization Networking: Plumbing of computing IEEE: Virtual Bridging, … IETF: Virtual Routers, … ITU: Mobile Virtual Operators, … Washington University in St. Louis http://www.cse.wustl.edu/~jain/tutorials/gitma15.htm ©2015 Raj Jain 10-3 WhatWhat areare Things?Things? Thing Not a computer Phone, watches, thermostats, cars, Electric Meters, sensors, clothing, band-aids, TV,… Anything, Anywhere, Anytime, Anyway, Anyhow (5 A’s) Ref: http://blog.smartthings.com/iot101/iot-adding-value-to-peoples-lives/ Washington University in St. Louis http://www.cse.wustl.edu/~jain/tutorials/gitma15.htm ©2015 Raj Jain 10-4 InternetInternet ofof ThingsThings Less than 1% of things around us is connected.
    [Show full text]
  • The Best Smart Home Devices for Your Google Home
    3/30/2018 The best Google Home devices: Philips Hue, Nest Cam IQ, and more - Business Insider The best smart home devices for your Google Home CHRISTIAN DE LOOPER, INSIDER PICKS JAN. 23, 2018, 3:00 PM The Insider Picks team writes about stuff we think you'll like. Business Insider has affiliate partnerships, so we get a share of the revenue from your purchase. Google/Business Insider The Insider Pick: http://www.businessinsider.com/best-google-home-devices?op=1&r=UK&IR=T/#the-best-light-bulbs-that-work-with-google-home-1 1/15 3/30/2018 The best Google Home devices: Philips Hue, Nest Cam IQ, and more - Business Insider If you’ve chosen the Google Assistant as your smart home helper, you need the best smart home products that work with the Google Home, Mini, and Max. We've tested smart light bulbs, outlets, light switches, security cameras, and thermostats to find the best ones you can buy. The smart home is on the rise, and there are a growing number of awesome smart home devices that will help you live like the Jetsons. Companies like Google, Amazon, and Apple are all working hard with partners to expand their smart home ecosystems and bring digital assistants like Google Assistant, Amazon's Alexa, and Apple's Siri into the home. Most Android fans have chosen Google Home as their go-to smart home system, and for good reason. It integrates well with other Google products, can be easily set up and controlled from your smartphone, and allows for a more uniform experience with the Google Assistant.
    [Show full text]
  • Google Nest FAQ's
    Google Nest FAQ’s Professional Installer October 2020 Let’s go! | Confidential and Proprietary | Do not distribute Welcome to the Google Nest FAQ’s Here you will find some Frequently Asked Questions from both Branch Staff and Installers. Please use this information to assist with Google Nest sales and questions. Need any help? For assistance with technical aspects related to the Google Nest product range, including installation and any other issues related to the Pro Portal, Pro Finder and Pro network, contact the Nest Pro support team: Contact Us form at pro.nest.com/support 0808 178 0546 Monday to Friday – 08:00‑19:00 Saturday to Sunday – 09:00‑17:00 For help to grow your business with Google Nest, product-specific questions and sales support,contact the Field team: [email protected] 07908 740 199 | Confidential and Proprietary | Do not distribute Topics to be covered Product-specific ● Nest Thermostats ● Nest Protect ● Nest Cameras ● Nest Hello video doorbell ● Nest Aware and Nest Aware Plus ● Nest Speakers and Display ● Nest Wi-Fi Other ● Nest Pro ● Returns and Faults ● General Questions ● Product SKUs ● Additional resources | Confidential and Proprietary | Do not distribute Nest Thermostats ● What’s the difference between Nest 3rd Gen Learning Thermostat and Nest Thermostat E? The 3rd Generation Nest Learning Thermostat is a dual channel (heating and hot water) and Nest Thermostat E is a single channel (heating only) as well as design, features, wiring and price. ● How many Thermostats does my customer need for a multi zone system? As the 3rd Gen Nest Learning Thermostat is a dual channel thermostat it will control both Heating and Hot Water.
    [Show full text]
  • A Case Study on Nest's “Smart Home Energy” Business Model: Based on Strategic Choices for Connected Product
    A CASE STUDY ON NEST’S “SMART HOME ENERGY” BUSINESS MODEL: BASED ON STRATEGIC CHOICES FOR CONNECTED PRODUCT SONG, MINZHEONG Hansei University E-mail: [email protected] Abstract - The purpose of this study is to investigate Nest’s business strategy, because simple Nest products have a big potential of connected one and have synergy with Google’s current assets such as artificial intelligence. It makes sense to be developing them together. The key activities based on strategic choices for monetizing connected product are investigated. Nest’s capacity and functionality is to offer a seamless integration of devices, platforms, and services and the “Works with Nest” offers an ecosystem fulfilling the needs of different partners. For utilizing and monetizing customer data, Nest also provides a seamless end-to-end customer experience supported by product incentives. Nest also introduces open APIs to connect its smart devices to the wider IoT and open to “If This, Then That.” With those activities Nest builds Nest homeincluding energy. In terms of smart home energy (SHE), all Nest products were designed to work together and if there is a carbon monoxide leak, the Nest Protect can send a command to the Nest Thermostat to turn off the heat. The Nest app also controls them from one single place. Thanks to the support of numerous partners and third-party developers, Nest has partnered with 32 energy providers as of 2017. These partners provide energy from renewable and non-renewable energy sources. Nest emphasizes how the consumer can benefit from sharing data with their energy and insurance provider.
    [Show full text]
  • 1-The Age of the Data Product
    M.Sc.IT Sem II Unit-IV Big Data and Analytics 1-The Age of the Data Product 1.1-What Is a Data Product? The traditional answer to this question is usually “any application that combines data and algorithms.” But frankly, if you’re writing software and you’re not combining data with algorithms, then what are you doing? After all, data is the currency of programming! More specifically, we might say that a data product is the combination of data with statistical algorithms that are used for inference or prediction. Many data scientists are also statisticians, and statistical methodologies are central to data science. Armed with this definition, you could cite Amazon recommendations as an example of a data product. Amazon examines items you’ve purchased, and based on similar purchase behavior of other users, makes recommendations. In this case, order history data is combined with recommendation algorithms to make predictions about what you might purchase in the future. You might also cite Facebook’s “People You May Know” feature because this product “shows you people based on mutual friends, work and education information ... [and] many other factors”—essentially using the combination of social network data with graph algorithms to infer members of communities. Indeed, defining data products as simply the combination of data with statistical algorithms seems to limit data products to single software instances (e.g., a web application), which hardly seems a revolutionary economic force. Although we might point to Google or others as large-scale economic forces, the combination of a web crawler gathering a massive HTML corpus with the PageRank algorithm alone does not create a data economy.
    [Show full text]
  • Aura: an Iot Based Cloud Infrastructure for Localized Mobile Computation Outsourcing
    Aura: An IoT based Cloud Infrastructure for Localized Mobile Computation Outsourcing Ragib Hasan, Md. Mahmud Hossain, and Rasib Khan ragib, mahmud, rasib @cis.uab.edu { } Department of Computer and Information Sciences University of Alabama at Birmingham, AL 35294, USA Abstract—Many applications require localized computation in near the client’s mobile phone so that all communications order to ensure better performance, security, and lower costs. In occur over one or two network hops from the client, and the recent years, the emergence of Internet-of-Things (IoT) devices data never has to traverse over the public Internet. On the has caused a paradigm shift in computing and communication. other hand, building cloud infrastructures is very expensive, IoT devices are making our physical environment and infrastruc- requiring hundreds of millions of dollars to set up and operate tures smarter, bringing pervasive computing to the mainstream. cloud data centers, making it impossible and economically With billions of such devices slated to be deployed in the next five years, we have the opportunity to utilize these devices in infeasible to have data centers located near clients. For such converting our physical environment into interactive, smart, and localized computing facilities, we need a lightweight system intelligent computing infrastructures. In this paper, we present for outsourced computation that can be incorporated into each Aura – a highly localized IoT based cloud computing model. Aura building or physical infrastructure, making it available at a close allows clients to create ad hoc clouds using the IoT and other distance from clients. To achieve this, in this paper, we present computing devices in the nearby physical environment, while Aura – a system for outsourced computation on lightweight ad providing the flexibility of cloud computing.
    [Show full text]
  • Big Data Insights Into Energy and Resource Usage in the Live-In Lab Apartments
    Big data insights into energy and resource usage in the Live-in Lab apartments Elisabeth Danielsson Amanda Koskinen Bachelor of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI-2016 SE-100 44 STOCKHOLM Abstract This report aims to find ways of getting insights into energy and resource usage in a home with big data, based on the project Live-in Lab. Live-in Lab is a project that aims to increase the innovation pace in the building and construction sector when developing environmental technology. This is done by conducting implementation and research at the same time in a living lab that will be the home of about 300 students. The main objectives of this report is to find out what is possible to measure in the apartments that is related to energy, with what technologies and how the data can be analyzed to generate maximal insight and utility. The methodology used originates from the field of product realization. A literature study was carried out to learn more about energy usage in a home, big data and state of the art of similar projects as well as available technologies that can be used to collect data with. The technologies were in- vestigated in four different levels; Social data and IoT, Infrastructure mediated systems, Direct environment components and Wearable devices. The result comprises eleven purposed solutions that get insights in pat- terns of water consumption, ventilation, light, movements inside and outside the apartment, consumption patterns among others. To be able to get max- imal insights and utility it was studied how the purposed solutions could be combined.
    [Show full text]
  • LINUX JOURNAL (ISSN 1075-3583) Is Published Monthly by Belltown Media, Inc., PO Box 980985, Houston, TX 77098 USA
    SSH TUNNELS AND ENCRYPTED VIDEO STREAMING ™ WATCH: ISSUE OVERVIEW V APRIL 2016 | ISSUE 264 LinuxJournal.com Since 1994: The Original Magazine of the Linux Community + STUNNEL Intro to Pandas The Python Data Analysis SECURITY Library for Databases A Look at printf A Super- Protect Useful Scripting Your Desktop Command Environment What’s the with Qubes Kernel Space of Democracy? BE SMART ABOUT CREATING A SMART HOME LJ264-April2016.indd 1 3/22/16 10:12 AM NEW! Self-Audit: Agile Checking Product Assumptions Development at the Door Practical books Author: Author: Ted Schmidt for the most technical Greg Bledsoe Sponsor: IBM Sponsor: people on the planet. HelpSystems Improve Finding Your Business Way: Mapping Processes with Your Network an Enterprise to Improve !""#$!%&'"( Job Scheduler Manageability Author: Author: Mike Diehl Bill Childers Sponsor: Sponsor: Skybot InterMapper DIY Combating Commerce Site Infrastructure Sprawl Author: Reuven M. Lerner Author: Sponsor: GeoTrust Bill Childers Sponsor: Puppet Labs Download books for free with a Get in the Take Control simple one-time registration. Fast Lane of Growing with NVMe Redis NoSQL http://geekguide.linuxjournal.com Author: Server Clusters Mike Diehl Author: Sponsor: Reuven M. Lerner Silicon Mechanics Sponsor: IBM & Intel LJ264-April2016.indd 2 3/22/16 10:12 AM NEW! Self-Audit: Agile Checking Product Assumptions Development at the Door Practical books Author: Author: Ted Schmidt for the most technical Greg Bledsoe Sponsor: IBM Sponsor: people on the planet. HelpSystems Improve Finding Your Business Way: Mapping Processes with Your Network an Enterprise to Improve !""#$!%&'"( Job Scheduler Manageability Author: Author: Mike Diehl Bill Childers Sponsor: Sponsor: Skybot InterMapper DIY Combating Commerce Site Infrastructure Sprawl Author: Reuven M.
    [Show full text]
  • Hardware Warranty Center - Product Documentation Help
    2/22/2021 Hardware Warranty Center - Product Documentation Help Hardware Warranty Center This article is about replacements under manufacturer's warranty. Learn how to le a claim using device protection from the Google Store (formerly Nexus Protect). You can also explore your options if you’re outside the manufacturer warranty. We want to make sure you know how to get help if your device or accessory is or becomes defective through no fault of your own. You may be able to return the device or accessory for a refund, exchange, or repair. Just tell us which device or accessory you have and we'll tell you who to contact for warranty claims. Nothing in this section affects your legal rights. Select your device or accessory Nest Speakers and Displays Google Home Google Home Mini Google Home Max Google Nest Mini (2nd gen) Google Nest Audio Google Nest Hub Google Nest Hub Max Streaming Chromecast Chromecast (3rd generation) Chromecast with Google TV Connectivity Google Wi Google Wi (refurbished and purchased on gStore) Google Nest Wi Safety and Security Nest Hello Nest Protect Nest Protect (refurbished and purchased on gStore) Nest Secure https://support.google.com/product-documentation/troubleshooter/3070579#ts=6012658%2C10346899 1/4 2/22/2021 Hardware Warranty Center - Product Documentation Help Cameras Nest Cam Indoor (refurbished and purchased on gStore) Nest Cam IQ Nest Cam Outdoor Nest Cam Nest Cam IQ Outdoor Thermostats Nest Thermostat If there are any differences between this online Limited Warranty and the Limited Warranty included in the box with your product then this online Limited Warranty will supersede the terms of your in-the-box warranty.
    [Show full text]
  • Copyrighted Material
    index A NFC (near field communication), A/B testing, 82–84 91–93 ACID (atomicity, consistency, isolation, RFID (radio-frequency durability) principles, 33 identification), 91–93 adjacent possible, 201–202 sensors, 90–91 Adner, Ron, The Wide Lens: A New Strategy for Innovation, 202 B The Age of the Platform (Simon), xv bad data, 162–163 Amazon Bayesian methods of analysis, 81–82 Best Buy price matching, 198 Beane, Billy, xv–xvi Big Data statistics, 98–99 Howe, Art, and, xvii–xviii Google and Amazon model, 127–128 sabermetrics and, xvi–xvii shipping fee error, 14 Best Buy, 198 analysis, 77–79 Bezos, Jeff, 19–20 A/B testing, 82–84 Bhambhri, Anjul, 17 data visualization, 84–86 BI (business intelligence), 68–70 heat maps, 86–87 Big Brother, 187 Tableau software, 85 Big Data time series analysis, 87–88 acceptance gains within Visually software, 85 organization, 171 predictive analytics, 100–102, 136–137 bad data and, 162–163 LDMU (Law of Diminishing capabilities, 79–80 Marginal Utility), 103 characteristics, 50–52 LLN (Law of Large Numbers), checklist, 177 102–103 community knowledge, 173 regression analysis, 80–82 as complement, 56–57 sentiment analysis, 97–98 completeness, 65–68 text analytics, 95–97 conferences and, 173 Anderson, Chris consumers, 63–64 Free: The Future of a Radical Price, 14 current presence, 50–51 “Tech Is Too Cheap to Meter,” 14 data model evolution, 172–173 Angry Birds, 62 definition, 49–50 Apache COPYRIGHTEDdynamism, MATERIAL 62 Cassandra, 124 evolution, 201–203 Hadoop (See Hadoop) experiments, 169–171 Apple, Big Data
    [Show full text]
  • CDP Climate Change Response 2021
    CDP Climate Change Response 2021 Published July 2021 Alphabet, Inc. - Climate Change 2021 C0. Introduction C0.1 (C0.1) Give a general description and introduction to your organization. This is our 15th consecutive year responding to the CDP Climate Change questionnaire. We began calculating our annual carbon footprint in 2006. Every year since 2009, we’ve publicly reported the results to CDP. As our founders Larry and Sergey wrote in the original founders' letter, "Google is not a conventional company. We do not intend to become one." That unconventional spirit has been a driving force throughout our history, inspiring us to tackle big problems, and invest in moonshots like artificial intelligence ("AI") research and quantum computing. Alphabet is a collection of businesses—the largest of which is Google—which we report as two segments: Google Services and Google Cloud. We report all non-Google businesses collectively as Other Bets. Our Other Bets include earlier stage technologies that are further afield from our core Google business. Our mission to organize the world’s information and make it universally accessible and useful is as relevant today as it was when we were founded in 1998. Since then, we’ve evolved from a company that helps people find answers to a company that helps you get things done. We’re focused on building an even more helpful Google for everyone, and we aspire to give everyone the tools they need to increase their knowledge, health, happiness and success. Google Services We have always been a company committed to building helpful products that can improve the lives of millions of people.
    [Show full text]