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[5/7] incubator-spot git commit: Replacing PHP index files with regular HTML due compatibility of extension on ASF

http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/blog/jupyter-notebooks-for-data-analysis/index.html
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diff --git a/blog/jupyter-notebooks-for-data-analysis/index.html b/blog/jupyter-notebooks-for-data-analysis/index.html
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+                    <main id="main" class="m-all t-2of3 d-5of7 cf" role="main" itemscope itemprop="mainContentOfPage" itemtype="http://schema.org/Blog">
+
+                        <article id="post-136" class="cf post-136 post type-post status-publish format-standard hentry category-uncategorized" role="article" itemscope itemprop="blogPost" itemtype="http://schema.org/BlogPosting">
+
+                            <header class="article-header entry-header">
+
+                                <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">Jupyter Notebooks for Data Analysis</h1>
+
+                                <p class="byline entry-meta vcard">
+
+                                    <time class="updated entry-time" datetime="2016-09-22" itemprop="datePublished">
+                                        September 22, 2016
+                                    </time>
+                                    </span>
+                                </p>
+
+                            </header>
+                            <section class="entry-content cf" itemprop="articleBody">
+                                <p>
+                                    <strong>Why Does Apache Spot Include iPython notebooks? </strong>
+                                </p>
+                                <p>
+                                    The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment \u2013 thus the \u2018i\u2019 in iPython.
+                                </p>
+                                <p>
+                                    The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.
+                                </p>
+                                <p>
+                                    Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.
+                                </p>
+                                <img src="../../library/images/iPython-1.png" alt="ipython" class="aligncenter size-full wp-image-140" />
+                                <p>
+                                    <strong>IPython with Apache Spot for Network Threat Detection</strong>
+                                </p>
+                                <p>
+                                    <em>NOTE:� This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em>
+                                </p>
+                                <p>
+                                    Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select \u2013 even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.
+                                </p>
+                                <p>
+                                    If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five \u2018W\u2019s.
+                                </p>
+                                <p>
+                                    <strong><em>What? </em></strong>
+                                </p>
+                                <p>
+                                    Maybe there\u2019s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.
+                                </p>
+                                <p>
+                                    <strong><em>Who?</em></strong>
+                                </p>
+                                <p>
+                                    Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don\u2019t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that\u2019s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.
+                                </p>
+                                <p>
+                                    <strong><em>When?</em></strong>
+                                </p>
+                                <p>
+                                    To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse \u2013 instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.
+                                </p>
+                                <p>
+                                    <strong><em>Where?</em></strong>
+                                </p>
+                                <p>
+                                    When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it\u2019s pointing to a country banned by your company, indicating a leak.
+                                </p>
+                                <p>
+                                    <strong><em>Why?</em></strong>
+                                </p>
+                                <p>
+                                    This answer to \u201cwhy\u201d will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor\u2019s infected machine that connected to your network.
+                                </p>
+                                <p>
+                                    <strong>How to Get Answers to the Five Ws Questions</strong>
+                                </p>
+                                <p>
+                                    All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.
+                                </p>
+                                <p>
+                                    Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation \u2013 or just keep it for future reference.
+                                </p>
+                                <p>
+                                    <strong>Wrap Up</strong>
+                                </p>
+                                <p>
+                                    This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You\u2019ll also see that the overall processing time is very short \u2013 thanks to the iPython notebook task parallelism ability.
+                                </p>
+                                <p>
+                                    We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you\u2019re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.
+                                </p>
+                            </section>
+                            
+                            <footer class="article-footer">
+            
+                              filed under: <a href="../../category/data-science/" rel="category tag">Data Science</a>, <a href="../../category/ipython-notebooks/" rel="category tag">Ipython Notebooks</a>, <a href="../../category/threat-analysis-tools/" rel="category tag">Threat Analysis Tools</a>
+                              
+                            </footer> 
+
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+
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+							<h4 class="widgettitle">Recent Posts</h4>
+							<ul>
+								<li>
+									<a href="../../blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</a>
+								</li>
+								<li>
+									<a href="../../blog/jupyter-notebooks-for-data-analysis/">Jupyter Notebooks for Data Analysis</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/">Apache Spot (Incubating) and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure</a>
+								</li>
+								<li>
+									<a href="../../blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot (Incubating) Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-3-most-asked-questions/">Apache Spot (Incubating): 3 Most-Asked Questions</a>
+								</li>
+							</ul>
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+								<li>
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+								</li>
+								<li>
+									<a href='../../2016/09/'>September 2016</a>
+								</li>
+								<li>
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+								</li>
+								<li>
+									<a href='../../2016/03/'>March 2016</a>
+								</li>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/blog/jupyter-notebooks-for-data-analysis/index.php
----------------------------------------------------------------------
diff --git a/blog/jupyter-notebooks-for-data-analysis/index.php b/blog/jupyter-notebooks-for-data-analysis/index.php
deleted file mode 100755
index 1931779..0000000
--- a/blog/jupyter-notebooks-for-data-analysis/index.php
+++ /dev/null
@@ -1,128 +0,0 @@
-<?php include '../../includes/config.php'; ?>
-<?php
-$bodyclass = "single single-post";
-$pageTitle = "Jupyter Notebooks for Data Analysis - Apache Spot";
-$description = "";
-$currentPage = 'blog';
-?>
-<?php include '../../includes/header.php'; ?>
-
-            <div id="content">
-
-                <div id="inner-content" class="wrap cf">
-
-                    <main id="main" class="m-all t-2of3 d-5of7 cf" role="main" itemscope itemprop="mainContentOfPage" itemtype="http://schema.org/Blog">
-
-                        <article id="post-136" class="cf post-136 post type-post status-publish format-standard hentry category-uncategorized" role="article" itemscope itemprop="blogPost" itemtype="http://schema.org/BlogPosting">
-
-                            <header class="article-header entry-header">
-
-                                <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">Jupyter Notebooks for Data Analysis</h1>
-
-                                <p class="byline entry-meta vcard">
-
-                                    <time class="updated entry-time" datetime="2016-09-22" itemprop="datePublished">
-                                        September 22, 2016
-                                    </time>
-                                    </span>
-                                </p>
-
-                            </header>
-                            <section class="entry-content cf" itemprop="articleBody">
-                                <p>
-                                    <strong>Why Does Apache Spot Include iPython notebooks? </strong>
-                                </p>
-                                <p>
-                                    The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment \u2013 thus the \u2018i\u2019 in iPython.
-                                </p>
-                                <p>
-                                    The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.
-                                </p>
-                                <p>
-                                    Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.
-                                </p>
-                                <img src="<?php echo BASE_URL;?>/library/images/iPython-1.png" alt="ipython" class="aligncenter size-full wp-image-140" />
-                                <p>
-                                    <strong>IPython with Apache Spot for Network Threat Detection</strong>
-                                </p>
-                                <p>
-                                    <em>NOTE:� This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em>
-                                </p>
-                                <p>
-                                    Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select \u2013 even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.
-                                </p>
-                                <p>
-                                    If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five \u2018W\u2019s.
-                                </p>
-                                <p>
-                                    <strong><em>What? </em></strong>
-                                </p>
-                                <p>
-                                    Maybe there\u2019s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.
-                                </p>
-                                <p>
-                                    <strong><em>Who?</em></strong>
-                                </p>
-                                <p>
-                                    Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don\u2019t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that\u2019s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.
-                                </p>
-                                <p>
-                                    <strong><em>When?</em></strong>
-                                </p>
-                                <p>
-                                    To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse \u2013 instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.
-                                </p>
-                                <p>
-                                    <strong><em>Where?</em></strong>
-                                </p>
-                                <p>
-                                    When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it\u2019s pointing to a country banned by your company, indicating a leak.
-                                </p>
-                                <p>
-                                    <strong><em>Why?</em></strong>
-                                </p>
-                                <p>
-                                    This answer to \u201cwhy\u201d will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor\u2019s infected machine that connected to your network.
-                                </p>
-                                <p>
-                                    <strong>How to Get Answers to the Five Ws Questions</strong>
-                                </p>
-                                <p>
-                                    All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.
-                                </p>
-                                <p>
-                                    Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation \u2013 or just keep it for future reference.
-                                </p>
-                                <p>
-                                    <strong>Wrap Up</strong>
-                                </p>
-                                <p>
-                                    This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You\u2019ll also see that the overall processing time is very short \u2013 thanks to the iPython notebook task parallelism ability.
-                                </p>
-                                <p>
-                                    We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you\u2019re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.
-                                </p>
-                            </section>
-                            
-                            <footer class="article-footer">
-            
-                              filed under: <a href="../../category/data-science/" rel="category tag">Data Science</a>, <a href="../../category/ipython-notebooks/" rel="category tag">Ipython Notebooks</a>, <a href="../../category/threat-analysis-tools/" rel="category tag">Threat Analysis Tools</a>
-                              
-                            </footer> 
-
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-
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-
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.html
----------------------------------------------------------------------
diff --git a/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.html b/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.html
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+
+                                <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</h1>
+
+                                <p class="byline entry-meta vcard">
+
+                                    <time class="updated entry-time" datetime="2016-10-21" itemprop="datePublished">
+                                        October 21, 2016
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+
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+                            <section class="entry-content cf" itemprop="articleBody">
+                                <p>
+                                    By Rob Kent, Vice President of Marketing at Cybraics
+                                </p>
+                                <p>
+                                    Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology. The problem is, with so much competition, are we losing sight of the real goal: protecting our customers against the adversary? With so much focus on competing and winning customers, are we negating one of the most fundamental advantages that we could have in the fight against cybercrime? Cooperation. Our adversaries are not shy about working together\u2026 the community is strong and growing, and while there is no doubt some healthy competition, the sharing of tools and techniques is certainly far more common than in the commercial world. Like Open Source software, security can only benefit and grow through peer-reviewed submissions. Citing Linus Law b
 y Eric S. Raymond from his book The Cathedral and the Bazaar, &#8220;Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix will be obvious to someone.&#8221;
+                                </p>
+                                <p>
+                                    This, of course, is not new information. People have been pointing this out for years. What is encouraging is that a few organizations are finally stepping up to the plate to try and change this paradigm in cybersecurity.  When it comes to cybersecurity, many have touted big data analytics as one of the key initiatives to combat adversaries. The problem is that big data in itself has its own set of challenges, one of the issues is that it is often looked at as a costly problem to store and scale as opposed to being used as another tool in an organization\u2019s arsenal. Apache Spot, a project pioneered by Intel and Cloudera, is aiming to fix this problem.
+                                </p>
+                                <p>
+                                    By creating an open source community, the hope is that identifying security threats within large data sets will become a manageable task for all organizations, despite their size or scale. Organizations overwhelmed with their data or those that aren\u2019t seeing results will have a community to turn to along with a common reference to compare results. By working together on Apache Spot, organizations will be able to share their experiences on how they\u2019ve tackled or how they need to tackle these issues with a common system to reference.  Data is never going to get smaller across organizations\u2019 core systems, so in many ways an organization just now beginning to dig into their data stores is basically starting from scratch. Having the ability to reference an open common model gives the opportunity for both those starting out and those seasoned, to have an open exchange of varying knowledge.
+                                </p>
+                                <p>
+                                    By supporting Apache Spot, Cybraics is hoping to learn as much as we contribute while we collaborate and take part in this Open Source Initiative. With the end result a set of guidelines for organizations to deploy a data analytics platform to find threats within their ecosystem, and a community that will share their experiences with the next generation of data and security professionals, perhaps we can all start actually working together to start leveling the playing field.
+                                </p>
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+									<a href="../../blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</a>
+								</li>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.php
----------------------------------------------------------------------
diff --git a/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.php b/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.php
deleted file mode 100755
index c86b518..0000000
--- a/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/index.php
+++ /dev/null
@@ -1,69 +0,0 @@
-<?php include '../../includes/config.php'; ?>
-<?php
-$bodyclass = "single single-post";
-$pageTitle = "Strength in Numbers: Why Consider Open Source Cybersecurity Analytics - Apache Spot";
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-
-                            <header class="article-header entry-header">
-
-                                <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</h1>
-
-                                <p class="byline entry-meta vcard">
-
-                                    <time class="updated entry-time" datetime="2016-10-21" itemprop="datePublished">
-                                        October 21, 2016
-                                    </time>
-                                    </span>
-                                </p>
-
-                            </header>
-                            <section class="entry-content cf" itemprop="articleBody">
-                                <p>
-                                    By Rob Kent, Vice President of Marketing at Cybraics
-                                </p>
-                                <p>
-                                    Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology. The problem is, with so much competition, are we losing sight of the real goal: protecting our customers against the adversary? With so much focus on competing and winning customers, are we negating one of the most fundamental advantages that we could have in the fight against cybercrime? Cooperation. Our adversaries are not shy about working together\u2026 the community is strong and growing, and while there is no doubt some healthy competition, the sharing of tools and techniques is certainly far more common than in the commercial world. Like Open Source software, security can only benefit and grow through peer-reviewed submissions. Citing Linus Law b
 y Eric S. Raymond from his book The Cathedral and the Bazaar, &#8220;Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix will be obvious to someone.&#8221;
-                                </p>
-                                <p>
-                                    This, of course, is not new information. People have been pointing this out for years. What is encouraging is that a few organizations are finally stepping up to the plate to try and change this paradigm in cybersecurity.  When it comes to cybersecurity, many have touted big data analytics as one of the key initiatives to combat adversaries. The problem is that big data in itself has its own set of challenges, one of the issues is that it is often looked at as a costly problem to store and scale as opposed to being used as another tool in an organization\u2019s arsenal. Apache Spot, a project pioneered by Intel and Cloudera, is aiming to fix this problem.
-                                </p>
-                                <p>
-                                    By creating an open source community, the hope is that identifying security threats within large data sets will become a manageable task for all organizations, despite their size or scale. Organizations overwhelmed with their data or those that aren\u2019t seeing results will have a community to turn to along with a common reference to compare results. By working together on Apache Spot, organizations will be able to share their experiences on how they\u2019ve tackled or how they need to tackle these issues with a common system to reference.  Data is never going to get smaller across organizations\u2019 core systems, so in many ways an organization just now beginning to dig into their data stores is basically starting from scratch. Having the ability to reference an open common model gives the opportunity for both those starting out and those seasoned, to have an open exchange of varying knowledge.
-                                </p>
-                                <p>
-                                    By supporting Apache Spot, Cybraics is hoping to learn as much as we contribute while we collaborate and take part in this Open Source Initiative. With the end result a set of guidelines for organizations to deploy a data analytics platform to find threats within their ecosystem, and a community that will share their experiences with the next generation of data and security professionals, perhaps we can all start actually working together to start leveling the playing field.
-                                </p>
-                            </section>
-                            <footer class="article-footer">
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-                                filed under: <a href="../../category/uncategorized/" rel="category tag">Uncategorized</a>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/cybersecurity/index.html
----------------------------------------------------------------------
diff --git a/category/cybersecurity/index.html b/category/cybersecurity/index.html
new file mode 100755
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+                        <h1 class="page-title">Category: Cybersecurity</h1>
+
+                        <article id="post-117" class="cf post-117 post type-post status-publish format-standard hentry category-cybersecurity" role="article">
+
+                            <header class="entry-header article-header">
+
+                                <h3 class="h2 entry-title"><a href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" rel="bookmark" title="Apache Spot and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure">Apache Spot (Incubating) and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure</a></h3>
+                                <p class="byline entry-meta vcard">
+                                    <time class="updated entry-time" datetime="2016-08-08" itemprop="datePublished">
+                                        August 8, 2016
+                                    </time>
+                                    </span>
+                                </p>
+
+                            </header>
+
+                            <section class="entry-content cf">
+
+                                <p>
+                                    The \u201cfirst\u201d documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls&#8230; <a class="excerpt-read-more" href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" title="Read Apache Spot and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure">Read more &raquo;</a>
+                                </p>
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+					
+						<div id="recent-posts-2" class="widget widget_recent_entries">
+							<h4 class="widgettitle">Recent Posts</h4>
+							<ul>
+								<li>
+									<a href="../../blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</a>
+								</li>
+								<li>
+									<a href="../../blog/jupyter-notebooks-for-data-analysis/">Jupyter Notebooks for Data Analysis</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/">Apache Spot (Incubating) and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure</a>
+								</li>
+								<li>
+									<a href="../../blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot (Incubating) Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-3-most-asked-questions/">Apache Spot (Incubating): 3 Most-Asked Questions</a>
+								</li>
+							</ul>
+						</div>
+						<div id="archives-2" class="widget widget_archive">
+							<h4 class="widgettitle">Archives</h4>
+							<ul>
+								<li>
+									<a href='../../2016/10/'>October 2016</a>
+								</li>
+								<li>
+									<a href='../../2016/09/'>September 2016</a>
+								</li>
+								<li>
+									<a href='../../2016/08/'>August 2016</a>
+								</li>
+								<li>
+									<a href='../../2016/03/'>March 2016</a>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/cybersecurity/index.php
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diff --git a/category/cybersecurity/index.php b/category/cybersecurity/index.php
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-<?php include '../../includes/config.php'; ?>
-<?php
-$bodyclass = "archive category";
-$pageTitle = "Cybersecurity - Apache Spot";
-$description = "";
-$currentPage = 'blog';
-?>
-<?php include '../../includes/header.php'; ?>
-
-            <div id="content">
-
-                <div id="inner-content" class="wrap cf">
-
-                    <main id="main" class="m-all t-2of3 d-5of7 cf" role="main" itemscope itemprop="mainContentOfPage" itemtype="http://schema.org/Blog">
-
-                        <h1 class="page-title">Category: Cybersecurity</h1>
-
-                        <article id="post-117" class="cf post-117 post type-post status-publish format-standard hentry category-cybersecurity" role="article">
-
-                            <header class="entry-header article-header">
-
-                                <h3 class="h2 entry-title"><a href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" rel="bookmark" title="Apache Spot and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure">Apache Spot (Incubating) and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure</a></h3>
-                                <p class="byline entry-meta vcard">
-                                    <time class="updated entry-time" datetime="2016-08-08" itemprop="datePublished">
-                                        August 8, 2016
-                                    </time>
-                                    </span>
-                                </p>
-
-                            </header>
-
-                            <section class="entry-content cf">
-
-                                <p>
-                                    The \u201cfirst\u201d documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls&#8230; <a class="excerpt-read-more" href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" title="Read Apache Spot and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure">Read more &raquo;</a>
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-<!-- end of site. what a ride! -->

http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/data-science/index.html
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diff --git a/category/data-science/index.html b/category/data-science/index.html
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+
+                        <h1 class="page-title">Category: Data Science</h1>
+
+                        <article id="post-136" class="cf post-136 post type-post status-publish format-standard hentry category-data-science category-ipython-notebooks category-threat-analysis-tools" role="article">
+
+                            <header class="entry-header article-header">
+
+                                <h3 class="h2 entry-title"><a href="../../blog/jupyter-notebooks-for-data-analysis/" rel="bookmark" title="Jupyter Notebooks for Data Analysis">Jupyter Notebooks for Data Analysis</a></h3>
+                                <p class="byline entry-meta vcard">
+                                    <time class="updated entry-time" datetime="2016-09-22" itemprop="datePublished">
+                                        September 22, 2016
+                                    </time>
+                                    </span>
+                                </p>
+
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+
+                            <section class="entry-content cf">
+
+                                <p>
+                                    Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython&#8230; <a class="excerpt-read-more" href="../../blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more &raquo;</a>
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+							<h4 class="widgettitle">Recent Posts</h4>
+							<ul>
+								<li>
+									<a href="../../blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/">Strength in Numbers:  Why Consider Open Source Cybersecurity Analytics</a>
+								</li>
+								<li>
+									<a href="../../blog/jupyter-notebooks-for-data-analysis/">Jupyter Notebooks for Data Analysis</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/">Apache Spot (Incubating) and Cybersecurity \u2014 Using NetFlows to Detect Threats to  Critical Infrastructure</a>
+								</li>
+								<li>
+									<a href="../../blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot (Incubating) Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
+								</li>
+								<li>
+									<a href="../../blog/apache-spot-3-most-asked-questions/">Apache Spot (Incubating): 3 Most-Asked Questions</a>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/data-science/index.php
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diff --git a/category/data-science/index.php b/category/data-science/index.php
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+++ /dev/null
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-<?php include '../../includes/config.php'; ?>
-<?php
-$bodyclass = "archive category";
-$pageTitle = "Data Science - Apache Spot";
-$description = "";
-$currentPage = 'blog';
-?>
-<?php include '../../includes/header.php'; ?>
-
-            <div id="content">
-
-                <div id="inner-content" class="wrap cf">
-
-                    <main id="main" class="m-all t-2of3 d-5of7 cf" role="main" itemscope itemprop="mainContentOfPage" itemtype="http://schema.org/Blog">
-
-                        <h1 class="page-title">Category: Data Science</h1>
-
-                        <article id="post-136" class="cf post-136 post type-post status-publish format-standard hentry category-data-science category-ipython-notebooks category-threat-analysis-tools" role="article">
-
-                            <header class="entry-header article-header">
-
-                                <h3 class="h2 entry-title"><a href="../../blog/jupyter-notebooks-for-data-analysis/" rel="bookmark" title="Jupyter Notebooks for Data Analysis">Jupyter Notebooks for Data Analysis</a></h3>
-                                <p class="byline entry-meta vcard">
-                                    <time class="updated entry-time" datetime="2016-09-22" itemprop="datePublished">
-                                        September 22, 2016
-                                    </time>
-                                    </span>
-                                </p>
-
-                            </header>
-
-                            <section class="entry-content cf">
-
-                                <p>
-                                    Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython&#8230; <a class="excerpt-read-more" href="../../blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more &raquo;</a>
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/index.html
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diff --git a/category/index.html b/category/index.html
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http://git-wip-us.apache.org/repos/asf/incubator-spot/blob/7bb4a301/category/index.php
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