Big Data Analy1cs. Radu State

Size: px
Start display at page:

Download "Big Data Analy1cs. Radu State"

Transcription

1 Big Data Analy1cs Radu State

2 Short Bio Master of Science in Engineering, Johns Hopkins University, USA Senior Researcher with INRIA, France Professor, (University of Nancy 1) Research Scien1st, SnT/UL in Netlab research group 6/11/14 NETLAB Presenta1on 2

3 Outline What is Big Data Big Data Analysis in team Call Details record at a country level Analysis of Network traffic research done at SnT 6/11/14 NETLAB Presenta1on 3

4 Big Data at a glance 6/11/14 NETLAB Presenta1on 4

5 What are Big Data Architectures? 6/11/14 NETLAB Presenta1on 5

6 Phases in Big Data Processing 6/11/14 NETLAB Presenta1on 6

7 Map- Reduce holy grail in Big Data 6/11/14 NETLAB Presenta1on 7

8 Big Data is more n simple HPC 6/11/14 NETLAB Presenta1on 8

9 The current Eco- System 6/11/14 NETLAB Presenta1on 9

10 Anomaly Detec1on in large scale CDR records David Goergen, Radu State, Thomas Engel and Veena MENDIRATTA (Bell Labs, USA) One country : Ivory Cost Time Period: to million users 1124 base sta1ons (for mobile communica1ons) More n 3 billions entries summarizing on a hourly basis SMS and Voice Calls mobile users tracked over se months with GPS and call records 6/11/14 NETLAB Presenta1on 10

11 What happened in Ivory Cost in 2012? 6/11/14 NETLAB Presenta1on 11

12 The silent base sta1ons 6/11/14 NETLAB Presenta1on 12

13 Strange calling behaviors..at 2 AM. 6/11/14 NETLAB Presenta1on 13

14 Where is most variability in data? PCA analysis on dura1on pcacall Variances /11/14 NETLAB Presenta1on 14

15 Cloud and Service Management ALTO solves general rendezvous problem: Given a choice of resources, which one is best candidate? Normalized costs: Type: What does cost represent? ( Air-miles, hop count,...) 15 - Numerical (virtual coordinates) - Ordinal(position-based preferences) ALTO client 2 main abstractions: - Network Map Datacenter 2 - Cost Map Datacenter 1 Dynamic network information Provisioning policies ALTO service discovery Network specified in terms of Partition/Provider ID (PID): aggregation of endpoints identified by a providerdefined network location identifier. Routing Graphics sources: protocols 6/11/14 IIT RTC conference Datacenter 3

16 Op1miza1on of service provisioning (Cloud, CDNs) over large ISPs and networks FCC Dataset specifica1on (200 GB/year) FCC has embarked on a na1onwide performance study of residen1al wireline broadband service easurement points (unit_ids) hroughput (b) Latency Aim is to use raw datasets from this Cablevision study f or a nalysis a nd Figure 6: Sharpe ratios for upload throughput and latency to create ALTO Charter Comcast topology map and a cost map from this Cox dataset portfolio is modeled as specific service of interest Embarq MCI Using a c anonical M ap- Reduce ( Big D ata) eking PIDs where nodes have a high upload throughput or os for upload bandwidth merger completed, it took about 5 months for operating Mediacom computa1onal paradigm on a Hadoop TimeWarner b). figurenodes showshave of merged network to start showing a positive DsThe where low capacities latency. cluster Windstream Cablevision Charter Comcast Cox Embarq MCI Mediacom TimeWarner Windstream Default Sharpe ratio (y-axis) growth again. The Sharpe ratio for foroura portfolio p, is computed by subtable III: Cost map for upload bandwidth (Cu ) Major o utcomes graphs (at y = 0) represents Calculating cost maps using Sharpe ratio (Equation acting risk-free rate of return (R f ) from rate of e investment no longer is 1) isfinancial modeling ith Sharpe ra1os to a set of all straightforward. Wewconsider P ID to be Cablevision Charter Comcast Cox Embarq MCI Mediacom TimeWarner Windstream Default e portfolio return itself (r and dividing by standard p ), ISPs build in third par1es ALTO maps that takes ompared in terms of absolute Table II. The upload throughput cost map, Cu, Cablevision eviation of individual portfolio ( p ), as shown in l(1): both bandwidth nd atency nto account 2 Charter volution for ISPs. returns is calculated from asharpe ratio iusing Equations and 3 Comcast s to explore here, which we below. Cox r R Embarq f w to create cost maps.sp = p (1) MCI (a) Upload Throughput 12 (b) Latency Mediacom p X any correlation between i i TimeWarner i 2 P ID, C = S W (2) i Figure ratios upload0.86 throughput u yhistorical among ISPs. However, Windstream : Sharpe for and latency averages alone might not be appropriate if m m=1 Ps deliver acceptable latency Table IV: Cost map for latency (Cl ) sociated standard deviations are high, because in such cases Relevant PHere, ublica1ons and submissions ent benchmark we establish. i C is upload throughput cost for each PID i, Figure 6 shows Sharpe ratios for upload bandwidth merger completed, it took about 5 months for operating e effective metric of interest is much u lower than historhroughput (Figure 6a), where (Figure 6a) and latency (Figure 6b). The figure shows I capacities of merged networkin tocstart showing a positive David G oergen, V eena B. M endirafa ( Bell L abs, U SA), R adu S tate T homas E ngel. den1fying abnormal pafern ellular which is calculated over summation of value of each alisps average. However, Sharpe ratio takes associatedi that deliver what we monthly evolution (x-axis) of Sharpe (y-axis) foreach our growth again. as a distance from default cost. The ratio latency cost for month s Sharpe ratio for PID i (S ) and multiplying by communica1on (IPTCOMM 2013) m i calculatedline in ainsimilar manner. 9 PID, ISPs.CThe graphs (at The y = computations 0) represents andard deviation in account, so it isflows a better candidate Calculating cost maps using Sharpe ratio (Equation peed. Second, a number of a weight l, ishorizontal associated with PID. To rewardofsharpe ratios Equations 2which and 3 result in: cost matrices shown in Aggrega1ng threshold atr risk-free investment no longer is, 1) is straightforward. We consider P ID to be a set of all costdavid Goergen Vijay K. Gurbani (Bell L abs, U SA), adu State Of m aps a nd c osts: large- scale broadband ent over year inaupload r approximating function. Higher Sharpe ratios are that are positive, we weigh sum by fraction ofiiimonths Tables and IVratios for Ccan Cl, respectively. In of absolute tables, ISPs in Table II. The upload throughput cost map, Cu, best option. These be compared in terms u and and Mediacom). This may be measurements or tmonths) he Applica1on Layer ratio Traffic but O p1miza1on ( ALTO) ppid rotocol. (RTC 2013) quivalent to high averages and small standard deviations. for first column source and remaining values, alsoi on temporal evolution for individual ISPs. (from last f12 that Sharpe PID isrepresents is calculated from Sharpe ratio using Equations 2 and 3 g and provisioning, although columns destination PID. Thus,toa explore host in PIDwhich Comcast There are some interesting trends here, we below. positive (i.e., it is above risk-free investment line). maller values for Sharpe ratios are due to eir high David Goergen, Vijay Gurbani (Bell Labs, USA), adu Sbefore tate or Thomas Engel. Making historical connec1ons: Building Applica1on will R always hosts discuss below prefer showing howintocomcast create first cost(cost: maps.0), ause for this. Alternatively, In ALTO, a lower value for a cost indicates a higher andard deviations, or to small averages. A negative Sharpe 12 followed by hosts in PID Mediacom (cost: 0.316) if it wants to (submifed to CNSM 2014) Layer Traffic Op1miza1on (ALTO) network and maps public broadband data X First,cost data doesfrom not show any correlation between end in Sharpe ratio for i preference forreturn traffic to be sentperform from a source to a destination. optimize upload bandwidth, or hosts in PID Time Warner 8i 2 P ID, Cui = Sm Wi (2) tio implies that risk-free rate of would upload throughput and latency among ISPs. However, mized by Time Warner and Therefore, actual cost for each PID is furr (cost: 0.32) if it wants to minimize latency. The costs serve m=1 calculated Figure 6b does show that most ISPs deliver acceptable latency etter portfolio being analyzed. ward than trend that starts around 6/11/14 to connect disconnected components of Figure according to previously risk-free investment benchmark we establish. as: Here, Cui is upload throughput cost for each PID i, 5; Figure 7 depicts links formed by peers in We create twountil costjuly maps, each cost map is specific to sustained uptick This is less case with upload throughput (Figure 6a), largest where which is calculated over summation of value of each PID is(comcast) to peers inamong or ISPs PIDs that based on what upload re a wider variation deliver we month s Sharpe ratio for PID i (S i ) and multiplying by ion of Insight Communica-Cost map, Cu is created for P2Pclass of applications. m bandwidth of Table III. The width of edges between 1 2consider i an cost 3 acceptable upload speed. Second, a number of a weight associated with PID. To reward Sharpe ratios 8i 2 P ID, Def ault_cost = dmax(c, C,..., C )e February 29, 2012 Insight u is a measure of preference; i.e., PIDs connected PIDs ass of applications that seeks out PIDs that host peers with u uisps (3) showi continuous improvement over year in upload that are positive, we weigh sum by fraction of months i est cable operator in US

17 Big Data and Security Research Questions: Securing Big Data architectures Which data is accessed and processed? What happens to outcome? How to protect data in motion? Big Data approaches for advanced security analytics and advanced Persistent Threat (APT) detection massive data collecting data and event tracking at large scale deeper analytics on unstructured data (honeypots, firewall, DNS); consolidated view of security-related information; real-time analysis of streaming AAA security data in order to profile human behavior in loop Predict attacker behavior on or targets J. François, S. Wang, R. State, and T. Engel, BotTrack: Tracking Botnets using NetFlow and PageRank, in IFIP/TC6 NETWORKING 2011, Springer, Ed., Valencia, Spain, May 2011 Wagner Cynthia, J. François, R. State, and T. Engel, Machine Learning Approach for IP-Flow Record Anomaly Detection, in IFIP/TC6 NETWORKING 2011, Springer, Ed., Valencia, Spain, May Hommes, Stefan State, Radu Zinnen, Andreas Engel, Thomas. Detection of Abnormal Behaviour in a Surveillance Environment Using Control Charts. IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS) (2011), no. 8th, pp S. Wang, R. State, M. Ourdane T. Engel. Mining NetFlow Records for Critical Network Activities. AIMS 2010: S. Wang, R. State, M. Ourdane T. Engel. FlowRank: ranking NetFlow records. IWCMC 2010: /11/14

18 Botnet tracking Jerome Francois, Radu State, Thomas Engel Botnets: Army of controlled compromised machines Powerful afack vector (spam, DDoS, espionage...) P2P Bots detec=on: Well interconnected machines to maintain underlying network Graph Analysis (PageRank/Google) Improvement by leveraging honeypots Source: hfp://www.csoonline.com/ar1cle/348317/what- a- botnet- looks- like, Scof Berinato Network Security 18

19 Botnet tracking BotTrack / BotCloud (MapReduce version): Results: Stealthy botnet detec1on (1% of IP addresses) High accuracy ~ 99% Scalability (60,000 flows / second) Publica=ons: BotTrack: Tracking Botnets Using NetFlow and PageRank, François J., Wang S., State R., Thomas E., IFIP Networking 2011 BotCloud: Detec<ng Botnets Using MapReduce, François J., Wang S., Bronzi W., State R., Engel T., IEEE Interna1onal Workshop on Informa1on Forensics and Security - WIFS' Network Security 19

20 Financial Data Analysis Research Ques1ons: Model complex rela1onships for co- lending in EU banking zone Modeling of financial instruments Mining of highly unstructured data formats Integrate economical (NYSE), regulatory (SEC) and media news (chairman, board member informa1on obtained from social media Twifer, Google Trend) Assess and model risk based on graph modeling and Distributed computa1ons Analyze loan interests rates (Libor) with respect to addi1onal loan rates and economic indicators. Expected Outcomes 6/11/14 Link to local (Luxembourg) economy Impact to EU regulatory bodies Figure 1: Co-lending network for that are connected to ones it is directly connected with. Therefore, even if a bank has very few co-lending relationships itself, it may impact entire system if it is connected to a few major lenders. Since matrix L represents pairwise connectedness of all banks, we may write impact of bank i on system as following equation: x i = N j=1 Lijxj, i. This may be compactly represented as x = L x, wherex =[x 1,x 2,...,x N] R N 1 and L R N N. We pre-multiply left-hand-side of equation above by a scalar λ to get λ x = L x, i.e.,an eigensystem. The principal eigenvector in this system gives loadings of each bank on main eigenvalue and represents influence of each bank on lending network. This is known as centrality vector in sociology literature [5] and delivers a measure of systemic effect a single bank may have on lending system. Federal regulators may use centrality scores of all banks to rank banks in terms of ir risk contribution to entire system and determine best allocation of supervisory attention. The data we use comprises a sample of loans filings made by financial institutions with SEC. Our data covers a Development of such ac1vi1es at SnT Figure 2: Co-lending networks for No1ce: Figure from (1) sented in Figure 1 for We see that re are three large components of co-lenders, and three hub banks, with connections to large components. There are also satellite colenders. In order to determine which banks in network are most likely to contribute to systemic failure, we compute normalized eigenvalue centrality score described previously, and report this for top 25 banks. These are presented in Table 1. The three nodes with highest centrality are seen to be critical hubs in network se are J.P. Morgan (node 143), Bank of America (node 29), and Citigroup (node 47). They are bridges between all banks, and contribute highly to systemic risk. Figure 2 shows how network evolves in four years after Comparing 2006 with 2005 (Figure 1), we see that re still are disjointed large components connected by a few central nodes. From 2007 onwards, as financial crisis begins to take hold, co-lending activity diminished

21 Ques1ons? 21 6/11/14

Making historical connections: Building Application Layer Traffic Optimization (ALTO) network and cost maps from public broadband data

Making historical connections: Building Application Layer Traffic Optimization (ALTO) network and cost maps from public broadband data Making historical connections: Building Application Layer Traffic Optimization (ALTO) network and cost maps from public broadband data Vijay K. Gurbani Bell Laboratories, Alcatel-Lucent Email: vijay.gurbani@alcatel-lucent.com

More information

Concept and Project Objectives

Concept and Project Objectives 3.1 Publishable summary Concept and Project Objectives Proactive and dynamic QoS management, network intrusion detection and early detection of network congestion problems among other applications in the

More information

LARGE-SCALE INTERNET MEASUREMENTS FOR DATA-DRIVEN PUBLIC POLICY. Henning Schulzrinne (+ Walter Johnston & James Miller) FCC & Columbia University

LARGE-SCALE INTERNET MEASUREMENTS FOR DATA-DRIVEN PUBLIC POLICY. Henning Schulzrinne (+ Walter Johnston & James Miller) FCC & Columbia University 1 LARGE-SCALE INTERNET MEASUREMENTS FOR DATA-DRIVEN PUBLIC POLICY Henning Schulzrinne (+ Walter Johnston & James Miller) FCC & Columbia University 2 Overview Why measure? Results of FCC MBA 2011 and 2012

More information

Ins+tuto Superior Técnico Technical University of Lisbon. Big Data. Bruno Lopes Catarina Moreira João Pinho

Ins+tuto Superior Técnico Technical University of Lisbon. Big Data. Bruno Lopes Catarina Moreira João Pinho Ins+tuto Superior Técnico Technical University of Lisbon Big Data Bruno Lopes Catarina Moreira João Pinho Mo#va#on 2 220 PetaBytes Of data that people create every day! 2 Mo#va#on 90 % of Data UNSTRUCTURED

More information

Presented by: Aaron Bossert, Cray Inc. Network Security Analytics, HPC Platforms, Hadoop, and Graphs Oh, My

Presented by: Aaron Bossert, Cray Inc. Network Security Analytics, HPC Platforms, Hadoop, and Graphs Oh, My Presented by: Aaron Bossert, Cray Inc. Network Security Analytics, HPC Platforms, Hadoop, and Graphs Oh, My The Proverbial Needle In A Haystack Problem The Nuclear Option Problem Statement and Proposed

More information

White Paper. Intelligence Driven. Security Monitoring. v.2.1.1. nexusguard.com

White Paper. Intelligence Driven. Security Monitoring. v.2.1.1. nexusguard.com White Paper 1 Intelligence Driven Security Monitoring v.2.1.1 Overview In today s hypercompetitive business environment, companies have to make swift and decisive decisions. Making the right judgment call

More information

INCREASE NETWORK VISIBILITY AND REDUCE SECURITY THREATS WITH IMC FLOW ANALYSIS TOOLS

INCREASE NETWORK VISIBILITY AND REDUCE SECURITY THREATS WITH IMC FLOW ANALYSIS TOOLS WHITE PAPER INCREASE NETWORK VISIBILITY AND REDUCE SECURITY THREATS WITH IMC FLOW ANALYSIS TOOLS Network administrators and security teams can gain valuable insight into network health in real-time by

More information

Measuring Broadband America

Measuring Broadband America A Report on Consumer Wireline Broadband Performance in the U.S. FCC s Office of Engineering and Technology and Consumer and Governmental Affairs Bureau Table of Contents Executive Summary Methodology Figure

More information

2014 Measuring Broadband America Fixed Broadband Report

2014 Measuring Broadband America Fixed Broadband Report 2014 Measuring Broadband America Fixed Broadband Report A Report on Consumer Fixed Broadband Performance in the U.S. FCC s Office of Engineering and Technology and Consumer and Governmental Affairs Bureau

More information

SQream Technologies Ltd - Confiden7al

SQream Technologies Ltd - Confiden7al SQream Technologies Ltd - Confiden7al 1 Ge#ng Big Data Done On a GPU- Based Database Ori Netzer VP Product 26- Mar- 14 Analy7cs Performance - 3 TB, 18 Billion records SQream Database 400x More Cost Efficient!

More information

Service Description DDoS Mitigation Service

Service Description DDoS Mitigation Service Service Description DDoS Mitigation Service Interoute, Walbrook Building, 195 Marsh Wall, London, E14 9SG, UK Tel: +800 4683 7681 Email: info@interoute.com Contents Contents 1 Introduction...3 2 An Overview...3

More information

QoE-Aware Multimedia Content Delivery Over Next-Generation Networks

QoE-Aware Multimedia Content Delivery Over Next-Generation Networks QoE-Aware Multimedia Content Delivery Over Next-Generation Networks M. Oğuz Sunay July 9, 2013 Second Romeo Workshop PAGE: 1 M. Oğuz Sunay, Özyeğin University Istanbul, July 9, 2013 Romeo High-quality

More information

Dr. John E. Kelly III Senior Vice President, Director of Research. Differentiating IBM: Research

Dr. John E. Kelly III Senior Vice President, Director of Research. Differentiating IBM: Research Dr. John E. Kelly III Senior Vice President, Director of Research Differentiating IBM: Research IBM Research Priorities Impact on IBM and the Marketplace Globalization and Leverage Balanced Research Agenda

More information

Botnet Detection Based on Degree Distributions of Node Using Data Mining Scheme

Botnet Detection Based on Degree Distributions of Node Using Data Mining Scheme Botnet Detection Based on Degree Distributions of Node Using Data Mining Scheme Chunyong Yin 1,2, Yang Lei 1, Jin Wang 1 1 School of Computer & Software, Nanjing University of Information Science &Technology,

More information

Internet Traffic and Content Consolidation

Internet Traffic and Content Consolidation Internet Traffic and Content Consolidation Craig Labovitz Chief Scientist, Arbor Networks S. Iekel-Johnson, D. McPherson Arbor Networks, Inc. J. Oberheide, F. Jahanian University of Michigan Talk Outline

More information

http://www.broadband.gov/plan/ ( NBP ). 4 See

http://www.broadband.gov/plan/ ( NBP ). 4 See ENDNOTES 1 John Horrigan and Ellen Satterwhite, Americans Perspectives on Online Connection Speeds for Home and Mobile Devices, 1 (FCC 2010), at http://hraunfoss.fcc.gov/edocs_public/attachmatch/doc-298516a1.doc

More information

Workshop on Infrastructure Security and Operational Challenges of Service Provider Networks

Workshop on Infrastructure Security and Operational Challenges of Service Provider Networks Workshop on Infrastructure Security and Operational Challenges of Service Provider Networks Farnam Jahanian University of Michigan and Arbor Networks IFIP Working Group 10.4 June 29-30, 2006 What s the

More information

STEALTHWATCH MANAGEMENT CONSOLE

STEALTHWATCH MANAGEMENT CONSOLE STEALTHWATCH MANAGEMENT CONSOLE The System by Lancope is a leading solution for network visibility and security intelligence across physical and virtual environments. With the System, network operations

More information

Splunk for Networking and SDN

Splunk for Networking and SDN Copyright 2013 Splunk Inc. Splunk for Networking and SDN Stela Udovicic Senior Product Marke?ng Manager, Splunk #splunkconf Legal No?ces During the course of this presenta?on, we may make forward- looking

More information

A New Era Of Analytic

A New Era Of Analytic Penang egovernment Seminar 2014 A New Era Of Analytic Megat Anuar Idris Head, Project Delivery, Business Analytics & Big Data Agenda Overview of Big Data Case Studies on Big Data Big Data Technology Readiness

More information

Testing & Assuring Mobile End User Experience Before Production. Neotys

Testing & Assuring Mobile End User Experience Before Production. Neotys Testing & Assuring Mobile End User Experience Before Production Neotys Agenda Introduction The challenges Best practices NeoLoad mobile capabilities Mobile devices are used more and more At Home In 2014,

More information

Internet Traffic Evolution 2007-2011

Internet Traffic Evolution 2007-2011 Internet Traffic Evolution 2007-2011 Craig Labovitz April 6, 2011 Talk Outline Four-year ongoing inter-domain traffic study Review of 2010 results (NANOG / IETF / SIGCOMM) Methodology Changing carrier

More information

An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks

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

More information

Huawei One Net Campus Network Solution

Huawei One Net Campus Network Solution Huawei One Net Campus Network Solution 2 引 言 3 园 区 网 面 临 的 挑 战 4 华 为 园 区 网 解 决 方 案 介 绍 6 华 为 园 区 网 解 决 方 案 对 应 产 品 组 合 6 结 束 语 Introduction campus network is an internal network of an enterprise or organization,

More information

Application and practice of parallel cloud computing in ISP. Guangzhou Institute of China Telecom Zhilan Huang 2011-10

Application and practice of parallel cloud computing in ISP. Guangzhou Institute of China Telecom Zhilan Huang 2011-10 Application and practice of parallel cloud computing in ISP Guangzhou Institute of China Telecom Zhilan Huang 2011-10 Outline Mass data management problem Applications of parallel cloud computing in ISPs

More information

DATA ANALYSIS II. Matrix Algorithms

DATA ANALYSIS II. Matrix Algorithms DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where

More information

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume

More information

Elevating Data Center Performance Management

Elevating Data Center Performance Management Elevating Data Center Performance Management Data Center innovation reduces operating expense, maximizes employee productivity, and generates new sources of revenue. However, many I&O teams lack proper

More information

Complex, true real-time analytics on massive, changing datasets.

Complex, true real-time analytics on massive, changing datasets. Complex, true real-time analytics on massive, changing datasets. A NoSQL, all in-memory enabling platform technology from: Better Questions Come Before Better Answers FinchDB is a NoSQL, all in-memory

More information

Network Performance Monitoring at Minimal Capex

Network Performance Monitoring at Minimal Capex Network Performance Monitoring at Minimal Capex Some Cisco IOS technologies you can use to create a high performance network Don Thomas Jacob Technical Marketing Engineer About ManageEngine Network Servers

More information

Mining NetFlow Records for Critical Network Activities

Mining NetFlow Records for Critical Network Activities Mining NetFlow Records for Critical Network Activities Shaonan Wang 1, Radu State 1, Mohamed Ourdane 2, and Thomas Engel 1 1 Faculty of Science, Technology and Communication, University of Luxembourg {shaonan.wang,radu.state,thomas.engel}@uni.lu

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Denial of Service Attacks and Resilient Overlay Networks

Denial of Service Attacks and Resilient Overlay Networks Denial of Service Attacks and Resilient Overlay Networks Angelos D. Keromytis Network Security Lab Computer Science Department, Columbia University Motivation: Network Service Availability Motivation:

More information

DDOS WALL: AN INTERNET SERVICE PROVIDER PROTECTOR

DDOS WALL: AN INTERNET SERVICE PROVIDER PROTECTOR Journal homepage: www.mjret.in DDOS WALL: AN INTERNET SERVICE PROVIDER PROTECTOR Maharudra V. Phalke, Atul D. Khude,Ganesh T. Bodkhe, Sudam A. Chole Information Technology, PVPIT Bhavdhan Pune,India maharudra90@gmail.com,

More information

III Big Data Technologies

III Big Data Technologies III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

Joint ITU-T/IEEE Workshop on Next Generation Optical Access Systems. DBA & QoS on the PON - Commonalities with Switching & Routing

Joint ITU-T/IEEE Workshop on Next Generation Optical Access Systems. DBA & QoS on the PON - Commonalities with Switching & Routing Joint ITU-T/IEEE Workshop on Next Generation Optical Access Systems DBA & QoS on the PON - Commonalities with Switching & Routing Howard Frazier, Technical Director Broadcom Corporation Agenda Passive

More information

Massive Cloud Auditing using Data Mining on Hadoop

Massive Cloud Auditing using Data Mining on Hadoop Massive Cloud Auditing using Data Mining on Hadoop Prof. Sachin Shetty CyberBAT Team, AFRL/RIGD AFRL VFRP Tennessee State University Outline Massive Cloud Auditing Traffic Characterization Distributed

More information

Scaling Big Data Mining Infrastructure: The Smart Protection Network Experience

Scaling Big Data Mining Infrastructure: The Smart Protection Network Experience Scaling Big Data Mining Infrastructure: The Smart Protection Network Experience 黃 振 修 (Chris Huang) SPN 主 動 式 雲 端 截 毒 技 術 架 構 師 About Me SPN 主 動 式 雲 端 截 毒 技 術 架 構 師 SPN Hadoop 基 礎 運 算 架 構 師 Hadoop in Taiwan

More information

ATLAS Internet Observatory Bandwidth Bandwagon: An ISOC Briefing Panel November 11, 2009, Hiroshima, Japan

ATLAS Internet Observatory Bandwidth Bandwagon: An ISOC Briefing Panel November 11, 2009, Hiroshima, Japan ATLAS Internet Observatory Bandwidth Bandwagon: An ISOC Briefing Panel November 11, 2009, Hiroshima, Japan Danny McPherson danny@arbor.net Chief Security Officer ATLAS Observatory Details ISP / Content

More information

CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA

CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA Professor Yang Xiang Network Security and Computing Laboratory (NSCLab) School of Information Technology Deakin University, Melbourne, Australia http://anss.org.au/nsclab

More information

Securing data centres: How we are positioned as your ISP provider to prevent online attacks.

Securing data centres: How we are positioned as your ISP provider to prevent online attacks. Securing data centres: How we are positioned as your ISP provider to prevent online attacks. Executive Summary In today s technologically-demanding world, an organisation that experiences any internet

More information

Multi-Datacenter Replication

Multi-Datacenter Replication www.basho.com Multi-Datacenter Replication A Technical Overview & Use Cases Table of Contents Table of Contents... 1 Introduction... 1 How It Works... 1 Default Mode...1 Advanced Mode...2 Architectural

More information

Availability Digest. www.availabilitydigest.com. Prolexic a DDoS Mitigation Service Provider April 2013

Availability Digest. www.availabilitydigest.com. Prolexic a DDoS Mitigation Service Provider April 2013 the Availability Digest Prolexic a DDoS Mitigation Service Provider April 2013 Prolexic (www.prolexic.com) is a firm that focuses solely on mitigating Distributed Denial of Service (DDoS) attacks. Headquartered

More information

Privacy- Preserving P2P Data Sharing with OneSwarm. Presented by. Adnan Malik

Privacy- Preserving P2P Data Sharing with OneSwarm. Presented by. Adnan Malik Privacy- Preserving P2P Data Sharing with OneSwarm Presented by Adnan Malik Privacy The protec?on of informa?on from unauthorized disclosure Centraliza?on and privacy threat Websites Facebook TwiFer Peer

More information

Protecting DNS Critical Infrastructure Solution Overview. Radware Attack Mitigation System (AMS) - Whitepaper

Protecting DNS Critical Infrastructure Solution Overview. Radware Attack Mitigation System (AMS) - Whitepaper Protecting DNS Critical Infrastructure Solution Overview Radware Attack Mitigation System (AMS) - Whitepaper Table of Contents Introduction...3 DNS DDoS Attacks are Growing and Evolving...3 Challenges

More information

The changing face of global data network traffic

The changing face of global data network traffic The changing face of global data network traffic Around the turn of the 21st century, MPLS very rapidly became the networking protocol of choice for large national and international institutions. This

More information

Internet Firewall CSIS 4222. Packet Filtering. Internet Firewall. Examples. Spring 2011 CSIS 4222. net15 1. Routers can implement packet filtering

Internet Firewall CSIS 4222. Packet Filtering. Internet Firewall. Examples. Spring 2011 CSIS 4222. net15 1. Routers can implement packet filtering Internet Firewall CSIS 4222 A combination of hardware and software that isolates an organization s internal network from the Internet at large Ch 27: Internet Routing Ch 30: Packet filtering & firewalls

More information

PALANTIR CYBER An End-to-End Cyber Intelligence Platform for Analysis & Knowledge Management

PALANTIR CYBER An End-to-End Cyber Intelligence Platform for Analysis & Knowledge Management PALANTIR CYBER An End-to-End Cyber Intelligence Platform for Analysis & Knowledge Management INTRODUCTION Traditional perimeter defense solutions fail against sophisticated adversaries who target their

More information

BITAG Publishes Report: Differentiated Treatment of Internet Traffic

BITAG Publishes Report: Differentiated Treatment of Internet Traffic 1550 Larimer Street, Suite 168 Denver, CO. 80202 BITAG Publishes Report: Differentiated Treatment of Internet Traffic Denver, CO (October 8, 2015): Today, the Broadband Internet Technical Advisory Group

More information

QRadar SIEM and FireEye MPS Integration

QRadar SIEM and FireEye MPS Integration QRadar SIEM and FireEye MPS Integration March 2014 1 IBM QRadar Security Intelligence Platform Providing actionable intelligence INTELLIGENT Correlation, analysis and massive data reduction AUTOMATED Driving

More information

Mobile Operator Big Data Analytics & Actions

Mobile Operator Big Data Analytics & Actions Success Story Mobile Operator Big Data Analytics & Actions Experiences and Benefits Achieved Agenda 1 2 Company Background Focus Customer Profile 3 The Solution Decision Process 4 5 6 The Solution The

More information

STATEMENT OF CRAIG LABOVITZ, PHD Co-Founder and CEO of DeepField Before the House Judiciary Committee Subcommittee on Regulatory Reform, Commercial

STATEMENT OF CRAIG LABOVITZ, PHD Co-Founder and CEO of DeepField Before the House Judiciary Committee Subcommittee on Regulatory Reform, Commercial STATEMENT OF CRAIG LABOVITZ, PHD Co-Founder and CEO of DeepField Before the House Judiciary Committee Subcommittee on Regulatory Reform, Commercial and Antitrust Law Hearing on Competition in the Video

More information

Peer-to-Peer Botnet Detection Using NetFlow Master Thesis

Peer-to-Peer Botnet Detection Using NetFlow Master Thesis Peer-to-Peer Botnet Detection Using NetFlow Master Thesis Connor Dillon System and Network Engineering University of Amsterdam July 11, 2014. Abstract.. Traditional botnets use a centralized communications

More information

How the Netflix ISP Speed Index Documents Netflix Congestion Problems

How the Netflix ISP Speed Index Documents Netflix Congestion Problems How the Netflix ISP Speed Index Documents Netflix Congestion Problems By Peter Sevcik June 2014 NetForecast Report NFR5117 2014 NetForecast, Inc. As of this writing, a comedic YouTube video featuring Netflix

More information

Disaster Recovery Design Ehab Ashary University of Colorado at Colorado Springs

Disaster Recovery Design Ehab Ashary University of Colorado at Colorado Springs Disaster Recovery Design Ehab Ashary University of Colorado at Colorado Springs As a head of the campus network department in the Deanship of Information Technology at King Abdulaziz University for more

More information

10 METRICS TO MONITOR IN THE LTE NETWORK. [ WhitePaper ]

10 METRICS TO MONITOR IN THE LTE NETWORK. [ WhitePaper ] [ WhitePaper ] 10 10 METRICS TO MONITOR IN THE LTE NETWORK. Abstract: The deployment of LTE increases dependency on the underlying network, which must be closely monitored in order to avert service-impacting

More information

Big Data in Enterprise challenges & opportunities. Yuanhao Sun 孙 元 浩 yuanhao.sun@intel.com Software and Service Group

Big Data in Enterprise challenges & opportunities. Yuanhao Sun 孙 元 浩 yuanhao.sun@intel.com Software and Service Group Big Data in Enterprise challenges & opportunities Yuanhao Sun 孙 元 浩 yuanhao.sun@intel.com Software and Service Group Big Data Phenomenon 1.8ZB in 2011 2 Days > the dawn of civilization to 2003 750M Photos

More information

Giving life to today s media distribution services

Giving life to today s media distribution services Giving life to today s media distribution services FIA - Future Internet Assembly Athens, 17 March 2014 Presenter: Nikolaos Efthymiopoulos Network architecture & Management Group Copyright University of

More information

2013 Measuring Broadband America February Report

2013 Measuring Broadband America February Report 2013 Measuring Broadband America February Report A Report on Consumer Wireline Broadband Performance in the U.S. FCC s Office of Engineering and Technology and Consumer and Governmental Affairs Bureau

More information

Hortonworks & SAS. Analytics everywhere. Page 1. Hortonworks Inc. 2011 2014. All Rights Reserved

Hortonworks & SAS. Analytics everywhere. Page 1. Hortonworks Inc. 2011 2014. All Rights Reserved Hortonworks & SAS Analytics everywhere. Page 1 A change in focus. A shift in Advertising From mass branding A shift in Financial Services From Educated Investing A shift in Healthcare From mass treatment

More information

Truffle Broadband Bonding Network Appliance

Truffle Broadband Bonding Network Appliance Truffle Broadband Bonding Network Appliance Reliable high throughput data connections with low-cost & diverse transport technologies PART I Truffle in standalone installation for a single office. Executive

More information

ENHANCED HYBRID FRAMEWORK OF RELIABILITY ANALYSIS FOR SAFETY CRITICAL NETWORK INFRASTRUCTURE

ENHANCED HYBRID FRAMEWORK OF RELIABILITY ANALYSIS FOR SAFETY CRITICAL NETWORK INFRASTRUCTURE ENHANCED HYBRID FRAMEWORK OF RELIABILITY ANALYSIS FOR SAFETY CRITICAL NETWORK INFRASTRUCTURE Chandana Priyanka G. H., Aarthi R. S., Chakaravarthi S., Selvamani K. 2 and Kannan A. 3 Department of Computer

More information

DOMINO Broadband Bonding Network

DOMINO Broadband Bonding Network 2 DOMINO AGGREGATION DE VOIES ETHERNET N 1 Bridging to the Future par [Hypercable] DOMINO DOMINO Broadband BondingTM Network Appliance With cellular data card failover/aggregation capability DANS CE NUMERO

More information

Craig Labovitz, Scott Iekel-Johnson, Danny McPherson Arbor Networks Jon Oberheide, Farnam Jahanian University of Michigan

Craig Labovitz, Scott Iekel-Johnson, Danny McPherson Arbor Networks Jon Oberheide, Farnam Jahanian University of Michigan Internet Inter-Domain Traffic Craig Labovitz, Scott Iekel-Johnson, Danny McPherson Arbor Networks Jon Oberheide, Farnam Jahanian University of Michigan Motivation Measuring the Internet is hard Significant

More information

A Review on Zero Day Attack Safety Using Different Scenarios

A Review on Zero Day Attack Safety Using Different Scenarios Available online www.ejaet.com European Journal of Advances in Engineering and Technology, 2015, 2(1): 30-34 Review Article ISSN: 2394-658X A Review on Zero Day Attack Safety Using Different Scenarios

More information

Timely Decisions with Big Data: Opportunities & Challenges

Timely Decisions with Big Data: Opportunities & Challenges IEEE CQR San Diego May 16, 2012 Timely Decisions with Big Data: Opportunities & Challenges Anukool Lakhina Guavus, Inc. GUAVUS INC. ALL RIGHTS RESERVED Double Clicking on Big Data Sources of Big Data:

More information

McAfee Network Security Platform

McAfee Network Security Platform McAfee Network Security Platform Next Generation Network Security Youssef AGHARMINE, Network Security, McAfee Network is THE Security Battleground Who is behind the data breaches? 81% some form of hacking

More information

Glasnost or Tyranny? You Can Have Secure and Open Networks!

Glasnost or Tyranny? You Can Have Secure and Open Networks! AT&T is a proud sponsor of StaySafe Online Glasnost or Tyranny? You Can Have Secure and Open Networks! Steven Hurst CISSP Director - AT&T Security Services and Technology AT&T Chief Security Office 2009

More information

Trends in Internet Traffic Patterns Darren Anstee, EMEA Solutions Architect

Trends in Internet Traffic Patterns Darren Anstee, EMEA Solutions Architect Trends in Internet Traffic Patterns Darren Anstee, EMEA Solutions Architect This Talk The End of the Internet as we Know it We present the largest study of Internet traffic every conducted Peer-reviewed

More information

VoIP- The New Voice of the Lightwave. Clifford Holliday. B & C Consulting Services

VoIP- The New Voice of the Lightwave. Clifford Holliday. B & C Consulting Services VoIP the New Voice of the Lightwave By Clifford R. Holliday B & C Consulting Services Abstracted from the author s Report Voice on the Lightwave VoIP; How Will VoIP Impact the Telecom Market? Available

More information

Hadoop s Advantages for! Machine! Learning and. Predictive! Analytics. Webinar will begin shortly. Presented by Hortonworks & Zementis

Hadoop s Advantages for! Machine! Learning and. Predictive! Analytics. Webinar will begin shortly. Presented by Hortonworks & Zementis Webinar will begin shortly Hadoop s Advantages for Machine Learning and Predictive Analytics Presented by Hortonworks & Zementis September 10, 2014 Copyright 2014 Zementis, Inc. All rights reserved. 2

More information

Practical Graph Mining with R. 5. Link Analysis

Practical Graph Mining with R. 5. Link Analysis Practical Graph Mining with R 5. Link Analysis Outline Link Analysis Concepts Metrics for Analyzing Networks PageRank HITS Link Prediction 2 Link Analysis Concepts Link A relationship between two entities

More information

BIG DATA IN BUSINESS ENVIRONMENT

BIG DATA IN BUSINESS ENVIRONMENT Scientific Bulletin Economic Sciences, Volume 14/ Issue 1 BIG DATA IN BUSINESS ENVIRONMENT Logica BANICA 1, Alina HAGIU 2 1 Faculty of Economics, University of Pitesti, Romania olga.banica@upit.ro 2 Faculty

More information

Innovation: Add Predictability to an Unpredictable World

Innovation: Add Predictability to an Unpredictable World Innovation: Add Predictability to an Unpredictable World Improve Visibility and Control of Your Telecom Network Judith Hurwitz President and CEO Sponsored by Hitachi Data Systems Introduction It is all

More information

CHAPTER 6. VOICE COMMUNICATION OVER HYBRID MANETs

CHAPTER 6. VOICE COMMUNICATION OVER HYBRID MANETs CHAPTER 6 VOICE COMMUNICATION OVER HYBRID MANETs Multimedia real-time session services such as voice and videoconferencing with Quality of Service support is challenging task on Mobile Ad hoc Network (MANETs).

More information

How Network Operators Do Prepare for the Rise of the Machines

How Network Operators Do Prepare for the Rise of the Machines Internet of Things and the Impact on Transport Networks How Network Operators Do Prepare for the Rise of the Machines Telecommunication networks today were never designed having Inter of Things use cases

More information

Big Graph Processing: Some Background

Big Graph Processing: Some Background Big Graph Processing: Some Background Bo Wu Colorado School of Mines Part of slides from: Paul Burkhardt (National Security Agency) and Carlos Guestrin (Washington University) Mines CSCI-580, Bo Wu Graphs

More information

Whitepaper. 10 Metrics to Monitor in the LTE Network. www.sevone.com blog.sevone.com info@sevone.com

Whitepaper. 10 Metrics to Monitor in the LTE Network. www.sevone.com blog.sevone.com info@sevone.com 10 Metrics to Monitor in the LTE Network The deployment of LTE increases dependency on the underlying network, which must be closely monitored in order to avert serviceimpacting events. In addition, the

More information

DoS: Attack and Defense

DoS: Attack and Defense DoS: Attack and Defense Vincent Tai Sayantan Sengupta COEN 233 Term Project Prof. M. Wang 1 Table of Contents 1. Introduction 4 1.1. Objective 1.2. Problem 1.3. Relation to the class 1.4. Other approaches

More information

Data Mining for Data Cloud and Compute Cloud

Data Mining for Data Cloud and Compute Cloud Data Mining for Data Cloud and Compute Cloud Prof. Uzma Ali 1, Prof. Punam Khandar 2 Assistant Professor, Dept. Of Computer Application, SRCOEM, Nagpur, India 1 Assistant Professor, Dept. Of Computer Application,

More information

Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome

Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Richard Breakiron Senior Director, Cyber Solutions Rbreakiron@vion.com Office: 571-353-6127 / Cell: 803-443-8002

More information

DDoS Protection. How Cisco IT Protects Against Distributed Denial of Service Attacks. A Cisco on Cisco Case Study: Inside Cisco IT

DDoS Protection. How Cisco IT Protects Against Distributed Denial of Service Attacks. A Cisco on Cisco Case Study: Inside Cisco IT DDoS Protection How Cisco IT Protects Against Distributed Denial of Service Attacks A Cisco on Cisco Case Study: Inside Cisco IT 1 Overview Challenge: Prevent low-bandwidth DDoS attacks coming from a broad

More information

Virtual Leased Line (VLL) for Enterprise to Branch Office Communications

Virtual Leased Line (VLL) for Enterprise to Branch Office Communications Virtual Leased Line (VLL) for Enterprise to Branch Office Communications Reliable high throughput data connections with low-cost & diverse transport technologies Executive Summary: The Truffle Broadband

More information

A Mock RFI for a SD-WAN

A Mock RFI for a SD-WAN A Mock RFI for a SD-WAN Ashton, Metzler & Associates Background and Intended Use After a long period with little if any fundamental innovation, the WAN is now the focus of considerable innovation. The

More information

PERFORMANCE OF MOBILE AD HOC NETWORKING ROUTING PROTOCOLS IN REALISTIC SCENARIOS

PERFORMANCE OF MOBILE AD HOC NETWORKING ROUTING PROTOCOLS IN REALISTIC SCENARIOS PERFORMANCE OF MOBILE AD HOC NETWORKING ROUTING PROTOCOLS IN REALISTIC SCENARIOS Julian Hsu, Sameer Bhatia, Mineo Takai, Rajive Bagrodia, Scalable Network Technologies, Inc., Culver City, CA, and Michael

More information

ProtectWise: Shifting Network Security to the Cloud Date: March 2015 Author: Tony Palmer, Senior Lab Analyst and Aviv Kaufmann, Lab Analyst

ProtectWise: Shifting Network Security to the Cloud Date: March 2015 Author: Tony Palmer, Senior Lab Analyst and Aviv Kaufmann, Lab Analyst ESG Lab Spotlight ProtectWise: Shifting Network Security to the Cloud Date: March 2015 Author: Tony Palmer, Senior Lab Analyst and Aviv Kaufmann, Lab Analyst Abstract: This ESG Lab Spotlight examines the

More information

Image Analytics on Big Data In Motion Implementation of Image Analytics CCL in Apache Kafka and Storm

Image Analytics on Big Data In Motion Implementation of Image Analytics CCL in Apache Kafka and Storm Image Analytics on Big Data In Motion Implementation of Image Analytics CCL in Apache Kafka and Storm Lokesh Babu Rao 1 C. Elayaraja 2 1PG Student, Dept. of ECE, Dhaanish Ahmed College of Engineering,

More information

Security Solutions for the New Threads

Security Solutions for the New Threads Security Solutions for the New Threads We see things others can t Pablo Grande Sales Director, SOLA pgrande@arbor.net What a CISO Is Looking For Show Progress on Response Time Measurably improve our incident

More information

locuz.com Big Data Services

locuz.com Big Data Services locuz.com Big Data Services Big Data At Locuz, we help the enterprise move from being a data-limited to a data-driven one, thereby enabling smarter, faster decisions that result in better business outcome.

More information

MetroNet6 - Homeland Security IPv6 R&D over Wireless

MetroNet6 - Homeland Security IPv6 R&D over Wireless MetroNet6 - Homeland Security IPv6 R&D over Wireless By: George Usi, President, Sacramento Technology Group and Project Manager, California IPv6 Task Force gusi@sactechgroup.com Acknowledgement Reference:

More information

Mucho Big Data y La Seguridad para cuándo?

Mucho Big Data y La Seguridad para cuándo? Mucho Big Data y La Seguridad para cuándo? Juan Carlos Vázquez Sales Systems Engineer, LTAM mayo 9, 2013 Agenda Business Drivers Big Security Data GTI Integration SIEM Architecture & Offering Why McAfee

More information

Take the Red Pill: Becoming One with Your Computing Environment using Security Intelligence

Take the Red Pill: Becoming One with Your Computing Environment using Security Intelligence Take the Red Pill: Becoming One with Your Computing Environment using Security Intelligence Chris Poulin Security Strategist, IBM Reboot Privacy & Security Conference 2013 1 2012 IBM Corporation Securing

More information

Akuda Labs. Leverages Peak Hosting s Operations-as-a-Service Managed Hosting Solution to Process Big Data Analytics 500 Faster without Big Costs

Akuda Labs. Leverages Peak Hosting s Operations-as-a-Service Managed Hosting Solution to Process Big Data Analytics 500 Faster without Big Costs Akuda Labs Leverages Peak Hosting s Operations-as-a-Service Managed Hosting Solution to Process Big Data Analytics 500 Faster without Big Costs INDUSTRY: BIG DATA ANALYTICS This case study provides a high-level

More information

Detecting Network Anomalies. Anant Shah

Detecting Network Anomalies. Anant Shah Detecting Network Anomalies using Traffic Modeling Anant Shah Anomaly Detection Anomalies are deviations from established behavior In most cases anomalies are indications of problems The science of extracting

More information

A Novel Distributed Denial of Service (DDoS) Attacks Discriminating Detection in Flash Crowds

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

More information

The Sumo Logic Solution: Security and Compliance

The Sumo Logic Solution: Security and Compliance The Sumo Logic Solution: Security and Compliance Introduction With the number of security threats on the rise and the sophistication of attacks evolving, the inability to analyze terabytes of logs using

More information

Tim Blevins Execu;ve Director Labor and Revenue Solu;ons. FTA Technology Conference August 4th, 2015

Tim Blevins Execu;ve Director Labor and Revenue Solu;ons. FTA Technology Conference August 4th, 2015 Tim Blevins Execu;ve Director Labor and Revenue Solu;ons FTA Technology Conference August 4th, 2015 Governance and Organiza;onal Strategy PaIerns of Fraud and Abuse in Government What tools can we use

More information

An Elastic and Adaptive Anti-DDoS Architecture Based on Big Data Analysis and SDN for Operators

An Elastic and Adaptive Anti-DDoS Architecture Based on Big Data Analysis and SDN for Operators An Elastic and Adaptive Anti-DDoS Architecture Based on Big Data Analysis and SDN for Operators Liang Xia Frank.xialiang@huawei.com Tianfu Fu Futianfu@huawei.com Cheng He Danping He hecheng@huawei.com

More information

NextGen Infrastructure for Big DATA Analytics.

NextGen Infrastructure for Big DATA Analytics. NextGen Infrastructure for Big DATA Analytics. So What is Big Data? Data that exceeds the processing capacity of conven4onal database systems. The data is too big, moves too fast, or doesn t fit the structures

More information