Data Science ATL Meetup - Risk I/O Security Data Science

•

4 gostaram•1,279 visualizações

This is a talk about data science operations and the applications of Risk I/Os insights to the security industry - how we went about mining insights from our large dataset

Internet

What Your Security
Data Isn’t
Telling You
@mroytman

Michael Roytman
Data Scientist, Risk I/O
M.S. Operations Research, Georgia Tech

PART 1:
!
DATA SCI OPS:
!
LESS IS MORE
!

LESS TOOLS
LESS DATA
LESS MODEL COMPLEXITY
MORE IMPACT
LESS DATA SCIENTISTS

Remove the Threat
Remediation
Accept the Risk
Repair the Vulnerability

“It is a capital mistake to theorize
before one has data.
!
!
!
!
Insensibly, one begins to twist
facts to suit theories, instead of
theories to suit facts.”

C(ommon) V(ulnerability) S(coring) S(ystem)
“CVSS is designed to rank information
system vulnerabilities”
Exploitability/Temporal (Likelihood)
Impact/Environmental (Severity)
The Good: Open, Standardized Scores

FAIL 1: A Priori Modeling
“Following up my previous email, I have tweaked my
equation to try to achieve better separation between
adjacent scores and to have CCC have a perfect (storm) 10
score...There is probably a way to optimize the problem
numerically, but doing trial and error gives one plausible set
of parameters...except that the scores of 9.21 and 9.54 are
still too close together. I can adjust x.3 and x.7 to get a
better separation . . .”

2: Data Fundamentalism
Since 2006 Vulnerabilities have declined by 26 percent.”
http://csrc.nist.gov/groups/SNS/rbac/documents/vulnerability-trends10.pdf
!
!
The total number of vulnerabilities in 2013 is up 16 percent
so far when compared to what we saw in the same time
period in 2012. ”
http://www.symantec.com/content/en/us/enterprise/other_resources/b-
intelligence_report_06-2013.en-us.pdf

I Love It When You Call Me Big Data
50,000,000 Live Vulnerabilities
1,500,000 Assets
2,000 Organizations

I Love It When You Call Me Big Data
15,000,000
Breaches

Baseline Allthethings
Probability
(You Will Be Breached On A Particular Open Vulnerability)?
=(Open Vulnerabilities | Breaches Occurred On Their CVE)
/(Total Open Vulnerabilities)
2%

Probability A Vuln Having Property X Has Observed Breaches
RANDOMVULN
CVSS 10
CVSS 9
CVSS 8
CVSS 6
CVSS 7
CVSS 5
CVSS 4
Has Patch
0.000 0.010 0.020 0.030 0.040

Counterterrorism
Known Groups
Surveillance
Threat Intel,
Analysts
Targets,
Layouts
Past
Incidents,
Close
Calls

Uh, Sports?
Opposing
Teams, Speciﬁc
Players
Gameplay
Scouting
Reports,
Gametape
Roster,
Player
Skills
Learning
from
Losing

Defend Like You’ve Done It Before
Groups,
Motivations
Exploits
Vulnerability
Deﬁnitions
Asset
Topology,
Actual Vulns
on System
Learning
from
Breaches

Probability A Vuln Having Property X Has Observed Breaches
RandomVuln
CVSS 10
Exploit DB
Metasploit
MSP+EDB
0.0 0.1 0.2 0.2 0.3

Data is Everything and Everything is Data
Spray and Pray = 2%
CVSS 10 = 4%
Metasploit and Exploit DB = 30%

Mais conteúdo relacionado

Destaque

RSA 2017 - Predicting Exploitability - With Predictions

Michael Roytman

Data science brings a huge promise to IT security and accordingly to the sprouting of DS teams across all enterprises, and numerous vendors. Indeed DS has the potential to transform the way security is done—yet, the secret sauce is how to do it in a way that actually provides clear value, embedded into the security workflow, and leverages the human knowledge in combined with the data. (Source: RSA USA 2016-San Francisco)

Data Science Transforming Security Operations

Priyanka Aash

Using Hadoop to Drive Down Fraud for Telcos

Cloudera, Inc.

The market is overflowing with vendors who are out to build—wherein, graphs are used in the Detection phase. This session showcases the collaborative efforts between Azure Security Data Science, Microsoft Research, Azure Security Assurance and Microsoft’s Threat Intelligence Center to explore the idea of using graphs during/after the Incident Response phase, wherein the IOCs have been (or in the process of being) collected. At the end of the session, audience will gain insights from their incident response process using open source tools and take steps towards automating them.

Transforming incident Response to Intelligent Response using Graphs

Ram Shankar Siva Kumar

us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-...

jzadeh

Merging fraud in a full IP environment

Stefano Maria De' Rossi

Icse15 Tech-briefing Data Science

CS, NcState

Wso2datasciencesummerschool20151 150714180825-lva1-app6892

WSO2

With 2015 just around the corner, the Pivotal Data Science team has been challenged to point its predictive inclinations toward spotting emerging trends in Data Science. With a global team of 30, doing innovative work in almost every vertical market, Pivotal’s data scientists have a rich view into the underlying trends and shifts impacting their craft. – Annika Jimenez, Kaushik Das and Hulya Farinas – share their insights on the key Data Science industry trends for the coming year. Every angle of Data Science is fair game: New use cases at the vertical level Analytical tool usage trends Implications of the shift in focus to model operationalization Meta observations about maturity of the craft Ethics evolution in Data Science Venture capital activity To watch the on-demand webinar, visit http://www.pivotal.io/agile/top-data-science-trends-for-2015-webinar

Top Data Science Trends for 2015

VMware Tanzu

Machine learning Mindmap

Yee Jie NG

Mapping (big) data science (15 dec2014)대학(원)생

Han Woo PARK

We use the terms “Big Data” and “Data Science” for use of data processing to make sense of the world around us. Spanning many fields, Big Data brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. These usecases use basic analytics, advanced statistical methods, and predictive technologies like Machine Learning. However, it is not just about crunching the data. Some usecases like Urban Planning can be slow, and there is enough time to process the data. However, with use cases like traffic, patient monitoring, surveillance the the value of results degrades much faster with time and needs results within milliseconds to seconds. Collecting data from many sources, cleaning them up, processing them using computation clusters, and doing all these fast is a major challenge. This talk will discuss motivation behind big data and data science and how it can make a difference. Then it will discuss the challenges, systems, and methodologies for implementing and sustaining a data science pipeline.

Data Science in the Real World: Making a Difference

Srinath Perera

Jim Geovedi - Machine Learning for Cybersecurity

idsecconf

Pivotal Digital Transformation Forum: Accelerate Time to Market with Business...

VMware Tanzu

[2A7]Linkedin'sDataScienceWhyIsItScience

NAVER D2

Machine Learning for Threat Detection

Napier University

Video (at YouTube) - http://bit.ly/19TNSTF Big Data Security Analytics, Data Science and Machine Learning are a few of the new buzzwords that have invaded out industry of late. Most of what we hear are promises of an unicorn-laden, silver-bullet panacea by heavy-handed marketing folks, evoking an expected pushback from the most enlightened members of our community. This talk will help parse what we as a community need to know and understand about these concepts and help understand where the technical details and actual capabilities of those concepts and also where they fail and how they can be exploited and fooled by an attacker. The talk will also share results of the author's current ongoing research (on MLSec Project) of applying machine learning techniques to information secuirty monitoring.

Applying Machine Learning to Network Security Monitoring - BayThreat 2013

Alex Pinto

Pivotal Digital Transformation Forum: Becoming a Data Driven Enterprise

VMware Tanzu

저성장 시대 데이터 경제만이 살길이다

eungjin cho

Pivotal Digital Transformation Forum: Data Science

VMware Tanzu

Destaque (20)

RSA 2017 - Predicting Exploitability - With Predictions

Data Science Transforming Security Operations

Using Hadoop to Drive Down Fraud for Telcos

Transforming incident Response to Intelligent Response using Graphs

us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-...

Merging fraud in a full IP environment

Icse15 Tech-briefing Data Science

Wso2datasciencesummerschool20151 150714180825-lva1-app6892

Top Data Science Trends for 2015

Machine learning Mindmap

Mapping (big) data science (15 dec2014)대학(원)생

Data Science in the Real World: Making a Difference

Jim Geovedi - Machine Learning for Cybersecurity

Pivotal Digital Transformation Forum: Accelerate Time to Market with Business...

[2A7]Linkedin'sDataScienceWhyIsItScience

Machine Learning for Threat Detection

Applying Machine Learning to Network Security Monitoring - BayThreat 2013

Pivotal Digital Transformation Forum: Becoming a Data Driven Enterprise

저성장 시대 데이터 경제만이 살길이다

Pivotal Digital Transformation Forum: Data Science

Semelhante a Data Science ATL Meetup - Risk I/O Security Data Science

Fix What Matters: BSidesDetroit 2014

Michael Roytman

BSidesSF 2014 Fix What Matters:Why CVSS Sucks

Ed Bellis

BsidesSF 2014 Fix What Matters

Michael Roytman

Fix What Matters

Ed Bellis

Attacker Behavior Boston Security Conference 2015

Michael Roytman

Security Metrics are often about the performance of information security professionals - traditional ones are centered around vulnerability close rates, timelines, or criticality ratings. But how does one measure if those metrics are the rights ones? How does one measure risk reduction, or how successful your metrics program is at operationalizing that which is necessary to prevent a breach? The data we'll explore defined the 2016 Verizon DBIR Vulnerabilities section. This talk will borrow concepts from epidemiology, repeated game theory, classical and causal probability theory in order to demonstrate some inventive metrics for evaluating vulnerability management strategies. Not all vulnerabilities are at risk of being breached. Not all people are at risk for catching the flu. By analogy, we are trying to be effective at catching the "disease" of vulnerabilities which are susceptible to breaches, and not all are. How do we determine what is truly critical? How do we determine if we are effective at remediating what is truly critical? Because the incidence of disease is unknown, the absolute risk can not be calculated. This talk will introduce some concepts from other fields for dealing with infosec uncertainty. Attackers are human too - and currently available data allows us to make some predictions about how they'll behave. And to predict is to prevent.

Chicago Security Meetup 08/2016

Michael Roytman

Vulnerability Management Nirvana: A Study in Predicting Exploitability When everything is a priority, nothing is. 15% or 10,000 vulnerabilities have a CVSS score of 10. Vendors and practitioners alike use CVSS or their own threat intelligence models to predict which vulnerabilities will be exploited next. We review current options, present a predictive data-driven prioritization model, and how attendees can get started using our approach in their vulnerability management program.

Vulnerability Management Nirvana - Seattle Agora - 18Mar16

Kymberlee Price

Introduction to the ethics of machine learning

Daniel Wilson

We cant hack ourselves secure

Eoin Keary

17ª edição da Security BSides São Paulo, uma conferência gratuita sobre segurança da informação e cultura hacker, também conhecida como BSidesSP. Desta vez, estivemos duplamente representados pelo nosso Head de Produto, Leonardo Pinheiro e pelo nosso Head of Threat and Detection Research, Rodrigo Montoro. Imperdível! ;) Ambos apresentaram a palestra "Exploit Prediction Scoring System (EPSS) – Aperfeiçoando a priorização de vulnerabilidades de forma efetiva". Confira!

Bsides SP 2022 - EPSS - Final.pptx

Clavis Segurança da Informação

Palmer Symposium

Ed Bellis

The Future of Advanced Analytics

Haystax Technology

Web security – application security roads to software security nirvana iisf...

Eoin Keary

We are experiencing unprecedented exponential change. In 2013, approximately 3 billion people were connected to the internet. By 2025, ideally 7 billion people will be connected to the internet. Changes on this scale will dramatically impact how people will interact and thrive in the global digital economy. Organizations need to be more resilient in the face of all the challenges that will continue to grow. Positive #ChangeAgents are needed across organizations, sectors, and communities to help make this happen, will you answer the call? This session will provide a rapid overview of existing and emergent challenges for all organizations regarding cyber-resiliency, and then look towards the future regarding what challenges organizations will confront with resiliency given the rise of machine learning, quantum computing, and unintended misuses of systems to spread misinformation or confusion. Cyber-resiliency is more than just cybersecurity. While it does include the usual “organizational hygiene” steps such as prevention, early detection, and rapid mitigation – resiliency also includes other organizational activities focused on adapting quickly and overcoming unforeseen events. The pace of unprecedented exponential changes require us all to be positive leaders in this important area. This session will emphasize and empower all participants to be positive #ChangeAgents within their organizations, sectors, and communities.

David Bray - Why Cyber-Resiliency Matters: Unprecedented Exponential Changes

SUCanadaSummit

We are meant to measure and manage data with more precision than ever before using Big Data. But companies are getting Hadoopy often with little or no consideration of security. Are we taking on too much risk too fast? This session explains how best to handle the looming Big Data risk in any environment. Better predictions and more intelligent decisions are expected from our biggest data sets, yet do we really trust systems we secure the least? And do we really know why "learning" machines continue to make amusing and sometimes tragic mistakes? Infosec is in this game but with Big Data we appear to be waiting on the sidelines. What have we done about emerging vulnerabilities and threats to Hadoop as it leaves many of our traditional data paradigms behind? This presentation, based on the new book "Realities of Big Data Security" takes the audience through an overview of the hardest big data protection problem areas ahead and into our best solutions for the elephantine challenges here today.

CONFidence 2014: Davi Ottenheimer Protecting big data at scale

PROIDEA

Risk metric frameworks cover most of the elements that organizations deal with from an operational perspective. We have identified a gap in those, in which social media activities are not represented well (albeit being the highest growing attack vector). In this talk we’ll present a social media risk metric framework that allows organizations to measure and track both individuals as well as 3rd party entities risk to the organization.

Social Media Risk Metrics

Iftach Ian Amit

DeepSec 2014 - The Measured CSO

Alexander Hutton

Less is More: Behind the Data at Risk I/O

Michael Roytman

Ransomware and Emerging Cyber Threats: Why It's More Than Just An IT Problem ...

Steve Fantauzzo

For decades, the security profession has relied on the best technology we had at the time to deflect the onslaught of what we faced daily in the way of virus and malware attacks. Now, as predicted by Thomas Kuhn in his book “The Structure of Scientific Revolutions, we’re seeing the dawn of a new day where AI’s machine learning and advanced mathematical algorithms now offer validated deflection rates, pre-execution, in the realm of 99%. This session will explore this new paradigm and how it will impact our future. Main points covered: • How did our profession change in the world of reactive detection? • How to escape the inertia that held us, prisoners? • What is the power of AI and machine learning? • What are the risks of this new technology? Presenter: Our presenter for this webinar, John McClurg serves as Vice President and Ambassador-At-Large of Cylance, where he is responsible for building Security and Trust programs & operational excellence efforts. Prior to Cylance, he served as the CSO of Dell, Honeywell, and Lucent and in the U.S. Intelligence Community, as a twice-decorated member of the Federal Bureau of Investigation (FBI). He also served as a Deputy Branch Chief of CIA where he helped to establish the new Counterespionage Group and was responsible for the management of complex counterespionage investigations. McClurg was voted one of America’s 25 most influential security professionals. Organizer: Ardian Berisha Date: October 25th, 2018 Recorded webinar link:

The Future of Security: How Artificial Intelligence Will Impact Us

PECB

Semelhante a Data Science ATL Meetup - Risk I/O Security Data Science (20)

Fix What Matters: BSidesDetroit 2014

BSidesSF 2014 Fix What Matters:Why CVSS Sucks

BsidesSF 2014 Fix What Matters

Fix What Matters

Attacker Behavior Boston Security Conference 2015

Chicago Security Meetup 08/2016

Vulnerability Management Nirvana - Seattle Agora - 18Mar16

Introduction to the ethics of machine learning

We cant hack ourselves secure

Bsides SP 2022 - EPSS - Final.pptx

Palmer Symposium

The Future of Advanced Analytics

Web security – application security roads to software security nirvana iisf...

David Bray - Why Cyber-Resiliency Matters: Unprecedented Exponential Changes

CONFidence 2014: Davi Ottenheimer Protecting big data at scale

Social Media Risk Metrics

DeepSec 2014 - The Measured CSO

Less is More: Behind the Data at Risk I/O

Ransomware and Emerging Cyber Threats: Why It's More Than Just An IT Problem ...

The Future of Security: How Artificial Intelligence Will Impact Us

Mais de Michael Roytman

Michael Roytman's CyberTech EU presentation. This was presented in October 2023 and includes data about vulnerabilities from 660 Cisco Vulnerability Management Customers. For a deeper dive, see the prioritization to prediction reports: https://www.cyentia.com/prioritization-to-prediction-v9/. All of the data is generated from aggregated data from Cisco VM (Kenna) customers, or from telemetry data from Cisco, Alienvault, Reversings Labs, etc.

CyberTechEurope.pptx

Michael Roytman

Security is all about reacting. It’s time to make some predictions. Michael Roytman explains how Kenna Security used the AWS Machine Learning platform to train a binary classifier for vulnerabilities, allowing the company to predict whether or not a vulnerability will become exploitable. Michael offers an overview of the process. Kenna enriches the data with more specific, nondefinitional-level data. 500 million live vulnerabilities and their associated close rates inform the epidemiological data, as well as “in the wild” threat data from AlienVault’s OTX and SecureWorks’s CTU, Reversing Labs, and ISC SANS. The company uses 70% of the national vulnerability database as its training dataset and generates over 20,000 predictions on the remainder of the vulnerabilities. It then measures specificity and sensitivity, positive predictive value, and false positive and false negative rates before arriving at an optimal decision cutoff for the problem.

O'Reilly Security New York - Predicting Exploitability Final

Michael Roytman

Data Metrics and Automation: A Strange Loop - SIRAcon 2015

Michael Roytman

Security Metrics are often about the performance of information security professionals - tranditional ones are centered around vulnerability close rates, timelines, or criticality ratings. But how does one measure if those metrics are the rights ones? How does one measure risk reduction, or how sucecssful your metrics program is at operationalizing that which is necessary to prevent a breach? Associated Discussion - http://www.irongeek.com/i.php?page=videos/bsideslasvegas2015/gt06-who-watches-the-watchers-metrics-for-security-strategy-michael-roytman

Who Watches the Watchers Metrics for Security Strategy - BsidesLV 2015 - Roytman

Michael Roytman

Risk IO Webisode 1: The Breach Landscape

Michael Roytman

A Heartbleed By Any Other Name - Data Driven Vulnerability Management

Michael Roytman

Measure What You FIx: Asset Risk Management Done Right

Michael Roytman

Fix What Matters: A Data Driven Approach to Vulnerability Management

Michael Roytman

Mais de Michael Roytman (8)

CyberTechEurope.pptx

O'Reilly Security New York - Predicting Exploitability Final

Data Metrics and Automation: A Strange Loop - SIRAcon 2015

Who Watches the Watchers Metrics for Security Strategy - BsidesLV 2015 - Roytman

Risk IO Webisode 1: The Breach Landscape

A Heartbleed By Any Other Name - Data Driven Vulnerability Management

Measure What You FIx: Asset Risk Management Done Right

Fix What Matters: A Data Driven Approach to Vulnerability Management

Último

APNIC Updates presented by Paul Wilson at ARIN 53

APNIC

Nagercoil Escorts Service Girl ^ 9332606886, WhatsApp Anytime Nagercoil

meghakumariji156

Call girls Service in Ajman 0505086370 Ajman call girls. Being an enthusiast of women I examined many call girls in the prior 3 months but never got that much pleasure from anyone. I'm facing challenges in my own relationship that is the reason I become close to such girls but when you cannot get peace even paying for women that thing hurts more. I chose This call girl agency and tried one of their Indian call girl she gave me real comfort and after one week of that service, I chose another Pakistani call girls and again then Russian. so overall my practice was magnificent. The price is also moderate per hour. The plus point is the girl comes instantly to your location doesn't matter you are in Ajman or al Nahda or Deira or any area she comes undeviatingly to your hotel room. Ajman call girls, call girls in Ajman, indian call girls in Ajman, pakistani call girls in Ajman, Ajman escorts, Ajman call girls number, indian call girls numbers, pakistani call girls numbers, paid sex in Ajman, girls for night in Ajman, night service in Ajman

Call girls Service in Ajman 0505086370 Ajman call girls

Monica Sydney

APNIC Policy Roundup, presented by Sunny Chendi at the 5th ICANN APAC-TWNIC E...

APNIC

Abu Dhabi Escorts Service 0508644382 Escorts in Abu Dhabi. A nutshell review for Hot Call girls in Abu Dhabi. My experience was superb with them this is the only recommended Call girls service in Abu Dhabi with verified Call girls. I am using their services from past 6 months they never ever disappointed me in any way. Let's just say if i asked them to provide me russian Call girls they fulfilled my request or even pakistani Call girls or indian Call girls in Abu Dhabi. They have their owen drivers who brings the Call girls in less time in any area of Abu Dhabi like as well. I'm writing here everything after experience their services in all conditions. So hopefully they will keeps working in the same way and makes more consumers like me. These guys are the best example of work with perfection. Pleased with Abu Dhabi Call girls and highly suggested to every one. Call girls whatsapp numbers in abu dhabi. Rent a girlfriend abu dhabi, stylish call girls abu dhabi any more than she had to. In a way, I guess I was the reason for her being like, Indian call girls abu dhabi, Indian call girl abu dhabi, indian call girls in abu dhabi, indian call girl agency abu dhabi, Pakistan call girls in abu dhabi, Pakistani call girl in abu dhabi, russian call girls in abu dhabi, russian call girl service in abu dhabi, indian call girls numbers abu dhabi, pakistani call girls number abu dhabi, teen call girls in abu dhabi, outcall call girls in abu dhabi Al Zahiyah Abu Dhabi Call Girls, Al Wahda Abu Dhabi Call Girls, Al Khalidiya Abu Dhabi Call Girls, Khalifa City Abu Dhabi Call Girls, Al Reem island Call Girls, Yas Island Call girls, Al lulu Island Call Girls, Nurai Island Call girls, Saadiyat Island Call Girls, Tourist Club Area Call Girls, Al Baraha Call Girls, Al Bateen Call Girls, Al Danah Call Girls, Al Dhafrah Call Girls, Al Falah City Call Girls, Al Ghadeer Call Girls she was, at least partially, and that made me feel even more responsible to help fix it. But I'd be lying if I didn't admit more sordid, and much more interesting, thoughts crept up right alongside that responsibility.

Abu Dhabi Escorts Service 0508644382 Escorts in Abu Dhabi

Monica Sydney

20240509 QFM015 Engineering Leadership Reading List April 2024.pdf

Matthew Sinclair

Vip Firozabad Phone 8250092165 Escorts Service At 6k To 30k Along With Ac Room

meghakumariji156

Indian Escort in Abu DHabi 0508644382 Abu Dhabi Escorts. A nutshell review for Hot Call girls in Abu Dhabi. My experience was superb with them this is the only recommended Call girls service in Abu Dhabi with verified Call girls. I am using their services from past 6 months they never ever disappointed me in any way. Let's just say if i asked them to provide me russian Call girls they fulfilled my request or even pakistani Call girls or indian Call girls in Abu Dhabi. They have their owen drivers who brings the Call girls in less time in any area of Abu Dhabi like as well. I'm writing here everything after experience their services in all conditions. So hopefully they will keeps working in the same way and makes more consumers like me. These guys are the best example of work with perfection. Pleased with Abu Dhabi Call girls and highly suggested to every one. Call girls whatsapp numbers in abu dhabi. Rent a girlfriend abu dhabi, stylish call girls abu dhabi any more than she had to. In a way, I guess I was the reason for her being like, Indian call girls abu dhabi, Indian call girl abu dhabi, indian call girls in abu dhabi, indian call girl agency abu dhabi, Pakistan call girls in abu dhabi, Pakistani call girl in abu dhabi, russian call girls in abu dhabi, russian call girl service in abu dhabi, indian call girls numbers abu dhabi, pakistani call girls number abu dhabi, teen call girls in abu dhabi, outcall call girls in abu dhabi Al Zahiyah Abu Dhabi Call Girls, Al Wahda Abu Dhabi Call Girls, Al Khalidiya Abu Dhabi Call Girls, Khalifa City Abu Dhabi Call Girls, Al Reem island Call Girls, Yas Island Call girls, Al lulu Island Call Girls, Nurai Island Call girls, Saadiyat Island Call Girls, Tourist Club Area Call Girls, Al Baraha Call Girls, Al Bateen Call Girls, Al Danah Call Girls, Al Dhafrah Call Girls, Al Falah City Call Girls, Al Ghadeer Call Girls she was, at least partially, and that made me feel even more responsible to help fix it. But I'd be lying if I didn't admit more sordid, and much more interesting, thoughts crept up right alongside that responsibility.

Indian Escort in Abu DHabi 0508644382 Abu Dhabi Escorts

Monica Sydney

在线制作约克大学毕业证（yu毕业证）在读证明认证可查【Q/微信741003700】原版仿制全套留学文凭材料（毕业证/成绩单（GPA成绩修改）/文凭学历证书/在读证明、毕业完成信、Offer录取通知书)；（真实可查）教育部学历认证、留信网认证、文凭认证、diploma、certificate、Degree、Transcript（实体公司，专业可靠）。 1:1完美还原海外各大学证书上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700。留学生归国服务中心面向美国英国澳洲加拿大留学生提供以下服务: 一、办理毕业证成绩单（学校原版1：1高仿真制作）二、留信认证（留学回国人员学历认证，网上存档可查，查到后付款）三、教育部学历认证（中国教育部留服中心存档可查,查到后付款）办理流程 1、收集客户办理信息； 2、客户付定金下单、公司确认到账转制作点做电子版； 3、电子版做好发给客户确认、电子版确认好转成品部做成品； 4、成品做好拍照或者视频确认再付余款； 5、快递给客户（国内顺丰，国外DHL）。本公司诚聘美国、加拿大、英国、新西兰、澳洲、法国、德国、新加坡各地代理人员，如果你有业余时间有兴趣就请联系我们校园代理，报酬丰厚。真诚期待您的加盟。请联系本公司学历认证顾问(QQ：741003700 微信：741003700)欢迎咨询！最专业的学历顾问，最有经验的顶尖OP为广大留学生的归国之路保卫护航！

在线制作约克大学毕业证（yu毕业证）在读证明认证可查

ydyuyu

Real Men Wear Diapers T Shirts sweatshirt

rahman018755

"Boost Your Digital Presence: Partner with a Leading SEO Agency"

growthgrids

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in- call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500 /in-call, ₱6000/out-call Body to body massage with sex: ₱3000/in-call Full night for one person: ₱7000/in-call, ₱10000/out-call Full night for more than 1 person : Contact us at 🔝 9953056974🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974🔝. Thank you for considering us

call girls in Anand Vihar (delhi) call me [🔝9953056974🔝] escort service 24X7

9953056974 Low Rate Call Girls In Saket, Delhi NCR

真实学历认证【q/微1954292140】【康考迪亚大学毕业证成绩单Offer】【q/微1954292140】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【q/微1954292140】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【q/微1954292140】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 → 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(Offer)康考迪亚大学毕业证学位证靠谱定制

pxcywzqs

20240507 QFM013 Machine Intelligence Reading List April 2024.pdf

Matthew Sinclair

Ballia Escorts Service Girl ^ 9332606886, WhatsApp Anytime Ballia

meghakumariji156

Story Board.pptxrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr

HenryBriggs2

20240508 QFM014 Elixir Reading List April 2024.pdf

Matthew Sinclair

pdfcoffee.com_business-ethics-q3m7-pdf-free.pdf

JOHNBEBONYAP1

Best SEO Services Company in Dallas | Best SEO Agency Dallas

Digicorns Technologies

哪里办理美国迈阿密大学毕业证(本硕)umiami在读证明存档可查【Q/微信741003700】原版仿制全套留学文凭材料（毕业证/成绩单（GPA成绩修改）/文凭学历证书/在读证明、毕业完成信、Offer录取通知书)；（真实可查）教育部学历认证、留信网认证、文凭认证、diploma、certificate、Degree、Transcript（实体公司，专业可靠）。 1:1完美还原海外各大学证书上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700。留学生归国服务中心面向美国英国澳洲加拿大留学生提供以下服务: 一、办理毕业证成绩单（学校原版1：1高仿真制作）二、留信认证（留学回国人员学历认证，网上存档可查，查到后付款）三、教育部学历认证（中国教育部留服中心存档可查,查到后付款）办理流程 1、收集客户办理信息； 2、客户付定金下单、公司确认到账转制作点做电子版； 3、电子版做好发给客户确认、电子版确认好转成品部做成品； 4、成品做好拍照或者视频确认再付余款； 5、快递给客户（国内顺丰，国外DHL）。本公司诚聘美国、加拿大、英国、新西兰、澳洲、法国、德国、新加坡各地代理人员，如果你有业余时间有兴趣就请联系我们校园代理，报酬丰厚。真诚期待您的加盟。请联系本公司学历认证顾问(QQ：741003700 微信：741003700)欢迎咨询！最专业的学历顾问，最有经验的顶尖OP为广大留学生的归国之路保卫护航！

哪里办理美国迈阿密大学毕业证(本硕)umiami在读证明存档可查

ydyuyu

Data Science ATL Meetup - Risk I/O Security Data Science

1. What Your Security Data Isn’t Telling You @mroytman

2. Michael Roytman Data Scientist, Risk I/O M.S. Operations Research, Georgia Tech

3. PART 1: ! DATA SCI OPS: ! LESS IS MORE !

4. LESS TOOLS LESS DATA LESS MODEL COMPLEXITY MORE IMPACT LESS DATA SCIENTISTS

5. SAY “BIG DATA” ONE MORE TIME

6. EVERYONE IS A DATA SCIENTIST

8. TAKE ONLY WHAT YOU NEED

9. PART 2: ! FIX WHAT MATTERS

10. Remove the Threat Remediation Accept the Risk Repair the Vulnerability

11. “It is a capital mistake to theorize before one has data. ! ! ! ! Insensibly, one begins to twist facts to suit theories, instead of theories to suit facts.”

12. C(ommon) V(ulnerability) S(coring) S(ystem) “CVSS is designed to rank information system vulnerabilities” Exploitability/Temporal (Likelihood) Impact/Environmental (Severity) The Good: Open, Standardized Scores

13. FAIL 1: A Priori Modeling “Following up my previous email, I have tweaked my equation to try to achieve better separation between adjacent scores and to have CCC have a perfect (storm) 10 score...There is probably a way to optimize the problem numerically, but doing trial and error gives one plausible set of parameters...except that the scores of 9.21 and 9.54 are still too close together. I can adjust x.3 and x.7 to get a better separation . . .”

14. 2: Data Fundamentalism Since 2006 Vulnerabilities have declined by 26 percent.” http://csrc.nist.gov/groups/SNS/rbac/documents/vulnerability-trends10.pdf ! ! The total number of vulnerabilities in 2013 is up 16 percent so far when compared to what we saw in the same time period in 2012. ” http://www.symantec.com/content/en/us/enterprise/other_resources/b- intelligence_report_06-2013.en-us.pdf

15. 3: Attackers Change Tactics Daily

16.

17.

18.

19.

20.

21.

22. Repair the Vulnerability

23. I Love It When You Call Me Big Data 50,000,000 Live Vulnerabilities 1,500,000 Assets 2,000 Organizations

24. I Love It When You Call Me Big Data 15,000,000 Breaches

25.

26.

27. Baseline Allthethings Probability (You Will Be Breached On A Particular Open Vulnerability)? =(Open Vulnerabilities | Breaches Occurred On Their CVE) /(Total Open Vulnerabilities) 2%

28. Probability A Vuln Having Property X Has Observed Breaches RANDOMVULN CVSS 10 CVSS 9 CVSS 8 CVSS 6 CVSS 7 CVSS 5 CVSS 4 Has Patch 0.000 0.010 0.020 0.030 0.040

29.

30. Counterterrorism Known Groups Surveillance Threat Intel, Analysts Targets, Layouts Past Incidents, Close Calls

31.

32. Uh, Sports? Opposing Teams, Speciﬁc Players Gameplay Scouting Reports, Gametape Roster, Player Skills Learning from Losing

33. Defend Like You’ve Done It Before Groups, Motivations Exploits Vulnerability Deﬁnitions Asset Topology, Actual Vulns on System Learning from Breaches

34. Probability A Vuln Having Property X Has Observed Breaches RandomVuln CVSS 10 Exploit DB Metasploit MSP+EDB 0.0 0.1 0.2 0.2 0.3

35.

36. Data is Everything and Everything is Data Spray and Pray = 2% CVSS 10 = 4% Metasploit and Exploit DB = 30%

37. www.risk.io/jobs @mroytman THANKS!

Data Science ATL Meetup - Risk I/O Security Data Science

Recomendados

Recomendados

Mais conteúdo relacionado

Destaque

Destaque (20)

Semelhante a Data Science ATL Meetup - Risk I/O Security Data Science

Semelhante a Data Science ATL Meetup - Risk I/O Security Data Science (20)

Mais de Michael Roytman

Mais de Michael Roytman (8)

Último

Último (20)

Data Science ATL Meetup - Risk I/O Security Data Science