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My keynote at NoSQL Now! on August 21st, 2013
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There is often a considerable delay between the discovery of a vulnerability and the issue of a patch. One mitigation strategy for this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but if one is available. Since application configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within applications, which enables them to be mechanically inserted with minimal knowledge of the application and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We designed and implemented Talos, a system that mechanically instrument SWRRs into a given application, and evaluate it on five popular Linux server applications. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1x times more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Zhen Huang
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment. In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates and prepare you for a Cloud Native environment. Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
An Introduction to Prometheus (GrafanaCon 2016)
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https://irjet.net/archives/V4/i2/IRJET-V4I2261.pdf
Online java compiler with security editor
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IRJET Journal
This is an in-depth guide on how to do excel-like row selection in jQuery DataTable. In the end, you'll master row selection.
How To Do Excel-Like Row Selection in jQuery DataTable?
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A great research on what is vulnerable on the net
Internet census 2012
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Giuliano Tavaroli
Our technology, work processes, and activities all are depend based on Operation Systems to be safe and secure. Join us virtually for our upcoming "The Hacking Games - Operation System Vulnerabilities" Meetup to learn how hacker can compromise Operation System, bypass AntiVirus protection layer and exploiting Linux eBPF.
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lior mazor
http://www.iosrjournals.org/iosr-jce/pages/v13i1.html
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Instrument production applications (both in AWS and on prem) with x-ray to collect live telemetry and latency metrics on your applications. You can also use it to debug live!
Deep Dive: AWS X-Ray London Summit 2017
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Randall Hunt
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment. In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
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Brian Brazil
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Time Series Anomaly Detection with Azure and .NETT
Time Series Anomaly Detection with Azure and .NETT
Marco Parenzan
A summary of server-side JavaScript weaknesses
Node.js security tour
Node.js security tour
Giacomo De Liberali
A birthmark is a set of characteristic possessed by a program that uniquely recognizes a program. Birthmark of the software is based on Heap Graph. It is generated by using Google Chrome Developer Tools when the program is in execution. Software’s behavioural structure is demonstrated in the heap graph. It describes how the objects are related to each other to deliver the desired functionality of the website. Our aim is to develop and evaluate a system that can find theft/similarity between websites by using Agglomerative Clustering and Improved Frequent Subgraph Mining. To identify if a website is using the original program’s code or its module, birthmark of the original program is explored in the suspected program’s heap graph.
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Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Swati Patel
This is an interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD) tool intended for verifying parallel applications. In this article you will learn about the history of creating RRD, its basic abilities and also about some other similar tools and the way they differ from RRD.
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
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PVS-Studio
Aspects to check on security in php
Secure programming with php
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Mohmad Feroz
Based on Anna University Syllabus.
Information Management 2marks with answer
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Security for AWS : Journey to Least Privilege (update)
Security for AWS : Journey to Least Privilege (update)
Security for AWS: Journey to Least Privilege
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Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
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Evolution of Monitoring and Prometheus (Dublin 2018)
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Hacking android apps by srini0x00
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Online java compiler with security editor
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How To Do Excel-Like Row Selection in jQuery DataTable?
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Internet census 2012
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Procuring the Anomaly Packets and Accountability Detection in the Network
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Deep Dive: AWS X-Ray London Summit 2017
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Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
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Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
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Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Secure programming with php
Secure programming with php
Information Management 2marks with answer
Information Management 2marks with answer
More from nathanmarz
Talk given in NYC on 7/20/2015
Demystifying Data Engineering
Demystifying Data Engineering
nathanmarz
Runaway complexity in Big Data... and a plan to stop it
Runaway complexity in Big Data... and a plan to stop it
nathanmarz
Storm: distributed and fault-tolerant realtime computation
Storm: distributed and fault-tolerant realtime computation
nathanmarz
ElephantDB
ElephantDB
nathanmarz
How BackType does a lot with a little. Presented at POSSCON ’11.
Become Efficient or Die: The Story of BackType
Become Efficient or Die: The Story of BackType
nathanmarz
The architectural principles behind building systems that scale to vast amounts of data and operate on that data in realtime. Presented at POSSCON '11.
The Secrets of Building Realtime Big Data Systems
The Secrets of Building Realtime Big Data Systems
nathanmarz
Visuals for the Cascalog workshop on February 19th, 2011.
Cascalog workshop
Cascalog workshop
nathanmarz
Presentation of Cascalog at Strange Loop on October 15th, 2010. http://github.com/nathanmarz/cascalog
Cascalog at Strange Loop
Cascalog at Strange Loop
nathanmarz
My talk about Cascalog at Hadoop Day in Seattle.
Cascalog at Hadoop Day
Cascalog at Hadoop Day
nathanmarz
Presentation about Cascalog, a Clojure-based query language for Hadoop.
Cascalog at May Bay Area Hadoop User Group
Cascalog at May Bay Area Hadoop User Group
nathanmarz
Presentation I gave at Bay Area Clojure Meetup Group on May 6th, 2010. Also demoed examples from introductory tutorial: http://nathanmarz.com/blog/introducing-cascalog/
Cascalog
Cascalog
nathanmarz
High level overview of Cascading.
Cascading
Cascading
nathanmarz
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(12)
Demystifying Data Engineering
Demystifying Data Engineering
Runaway complexity in Big Data... and a plan to stop it
Runaway complexity in Big Data... and a plan to stop it
Storm: distributed and fault-tolerant realtime computation
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ElephantDB
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Become Efficient or Die: The Story of BackType
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The Secrets of Building Realtime Big Data Systems
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Cascalog at Strange Loop
Cascalog at Strange Loop
Cascalog at Hadoop Day
Cascalog at Hadoop Day
Cascalog at May Bay Area Hadoop User Group
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Cascalog
Cascalog
Cascading
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Your Code is Wrong
1.
Your Code is
Wrong Nathan Marz @nathanmarz 1
2.
Let’s start with
an example
3.
Storm’s “reportError” method
4.
(Storm is a
realtime computation system, like Hadoop but for realtime)
5.
Storm architecture
6.
Storm architecture Master node
(similar to Hadoop JobTracker)
7.
Storm architecture Used for
cluster coordination
8.
Storm architecture Run worker
processes
9.
Storm’s “reportError” method
10.
Used to show
errors in the Storm UI
11.
Error info is
stored in Zookeeper
12.
What happens when
a user deploys code like this?
13.
Denial-of-service on Zookeeper and
cluster goes down
14.
Robust! Designed input space
Actual input space
15.
Your code is
wrong
16.
Your code is
literally wrong
17.
Your code is
wrong
18.
19.
Why do you
believe your code is correct?
20.
Your code Dependency 1 Dependency
2 Dependency 3
21.
Dependency 1 Dependency 4 Dependency
5
22.
Dependency 4 Dependency 6 Dependency
9 Dependency 7 Dependency 8
23.
Dependency 3,000,000 Hardware
24.
Electronics
25.
Chemistry
26.
Atomic physics
27.
Quantum mechanics
28.
I think I
can safely say that nobody understands quantum mechanics. Richard Feynman
29.
Your code is
wrong
30.
Your code ...
31.
All the software
you’ve used has had bugs in it
32.
Including the software you’ve
written
33.
Your code is sometimes
correct
34.
That’s good enough!
35.
36.
Treat code as
nondeterministic
37.
Embrace “your code
is wrong” to design better software
38.
Robust! Designed input space
Actual input space
39.
Robust! Designed input space
Actual input space
40.
An example
41.
Learning from Hadoop Jobtracker Job Job Job
42.
Learning from Hadoop Jobtracker Job Job Job
43.
Learning from Hadoop Jobtracker Job Job Job
44.
Your code is
wrong
45.
So your processes
will crash
46.
Storm’s daemons are process
fault-tolerant
47.
Storm Nimbus Topology Topology Topology
48.
Storm Nimbus Topology Topology Topology
49.
Storm Nimbus Topology Topology Topology
50.
Storm Nimbus Topology Topology Topology
51.
Storm Nimbus Topology Topology Topology
52.
Robust! Designed input space
Actual input space
53.
Robust! Designed input space
Actual input space
54.
The impact of
code being wrong
55.
Robust! Designed input space
Actual input space Failures! Bad performance! Security holes! Irrelevant!
56.
Design principle #1 Measuring
and monitoring are the foundation of solid engineering
57.
Measuring: Under what range
of inputs does my software function well?
58.
Monitoring: What’s the actual
input space of my software?
59.
Measure & Monitor Latency Throughput Stack
traces Buffer sizes Memory usage CPU usage #threads spawned ...
60.
How you monitor
your software is as important as its functionality
61.
Design principle #2 Embrace
immutability
62.
Read/write database Application
63.
MySQLApplication
64.
MongoDBApplication
65.
RiakApplication
66.
CassandraApplication
67.
HBaseApplication
68.
Your code is
wrong
69.
So data will
be corrupted
70.
And you may
not know why
71.
Views Immutable, ever-growing data Application Architecture based on
immutability
72.
Views Immutable, ever-growing data Application Lambda architecture
73.
Design principle #3 Minimize
dependencies
74.
The less that
can go wrong, the less that will go wrong
75.
Example: Storm’s usage of
Zookeeper
76.
Worker locations stored
in Zookeeper
77.
All workers must
know locations of other workers to send messages
78.
Two ways to
get location updates
79.
1. Poll Zookeeper Worker
Zookeeper
80.
2. Use Zookeeper
“watch” feature to get push notifications Worker Zookeeper
81.
Method 2 is
faster but relies on another feature
82.
Storm uses both
methods Worker Zookeeper
83.
If watch feature
fails, locations still propagate via polling
84.
Eliminating dependence justified by
small amount of code required
85.
Design principle #4 Explicitly
respect functional input ranges
86.
Storm’s “reportError” method
87.
Implement self-throttling to avoid
overloading other systems
88.
Design principle #5 Embrace
recomputation
89.
“Your code is
wrong” meanings 1. Design input space differs from actual input space 2. The logic of your code is wrong 3. Requirements are constantly changing
90.
You must be
able to change your code to match shifting requirements
91.
Example: blogging software
92.
New requirement: search
93.
Have to build
a search index
94.
95.
Recomputation gives you so
much more
96.
Views Immutable, ever-growing data Application
97.
Building software no
different than any other engineering
98.
The underlying challenges are
the same
99.
100.
101.
What will break
it?
102.
What are limits
of my dependencies?
103.
How can I
add redundancy to increase robustness?
104.
Can I isolate
failures?
105.
Our raw materials
are ideas instead of matter
106.
Thank you
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