diff --git a/_config.yml b/_config.yml index c05dd4c..912bb76 100644 --- a/_config.yml +++ b/_config.yml @@ -10,6 +10,7 @@ url: "http://goeagle.io" # the base hostname & protocol for your site # Build settings markdown: kramdown +permalink: date documentations: - category: Getting Started @@ -50,11 +51,7 @@ documentations: url: /docs/tutorial/classification.html - category: Advanced links: - - title: Policy API Design - url: /docs/alert-api.html - - title: Alert API Design - url: /docs/alert-api.html - - title: Query API Design - url: /docs/userprofile.html - - title: User Profile Design - url: /docs/userprofile.html + - title: Eagle Architecture Highlights + url: /docs/architecture-highlights.html + - title: User Profile Machine Learning + url: /docs/user-profile-ml.html \ No newline at end of file diff --git a/_includes/head.html b/_includes/head.html index 6cd2c88..8efd94d 100644 --- a/_includes/head.html +++ b/_includes/head.html @@ -13,6 +13,9 @@ + + + diff --git a/_includes/header.html b/_includes/header.html index b15bf19..b4c2a0b 100644 --- a/_includes/header.html +++ b/_includes/header.html @@ -11,15 +11,16 @@ - - - +
  • +
    • + --> + diff --git a/_layouts/page.html b/_layouts/page.html index 2ecef52..ae86c06 100644 --- a/_layouts/page.html +++ b/_layouts/page.html @@ -21,6 +21,13 @@

    Secure Hadoop in Real Time

    +
    + + +
    diff --git a/_posts/2015-09-23-welcome-to-jekyll.markdown b/_posts/2015-09-23-welcome-to-jekyll.markdown index aed2fbd..5fa5f79 100644 --- a/_posts/2015-09-23-welcome-to-jekyll.markdown +++ b/_posts/2015-09-23-welcome-to-jekyll.markdown @@ -2,7 +2,7 @@ layout: post title: "Eagle" date: 2015-09-23 19:24:33 -categories: documentation +categories: post --- You’ll find this post in your `_posts` directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run `jekyll serve`, which launches a web server and auto-regenerates your site when a file is updated. diff --git a/architecture-highlights.md b/architecture-highlights.md new file mode 100644 index 0000000..7cf5ea1 --- /dev/null +++ b/architecture-highlights.md @@ -0,0 +1,29 @@ +--- +layout: doc +title: "Eagle Architecture Highlights" +permalink: /docs/architecture-highlights.html +--- + +> Eagle is an open-source Data Activity Monitoring solution for Hadoop to instantly detect access to sensitive data or malicious activities, and to take appropriate actions. + +![](/images/docs/eagle-highlights.png) + +### Data Collection and Storage + +Eagle provides programming API for extending Eagle to integrate any data source into Eagle policy evaluation framework. For example, Eagle hdfs audit monitoring collects data from Kafka which is populated from namenode log4j appender or from logstash agent. Eagle hive monitoring collects hive query logs from running job through YARN API, which is designed to be scalable and fault-tolerant. + +Eagle uses HBase as storage for storing metadata and metrics data, and also supports relational database through configuration change. + +### Data Processing + +**Stream processing API**: Eagle provides a stream processing API which is an abstraction on Apache Storm, but is also extensible to other streaming engines. This abstraction allows developers to easily assemble data transformation, filtering, external data join, etc. without being physically bound to a specific streaming platform. The Eagle streaming API also allows developers to easily integrate business logic with the Eagle policy engine. Internally, the Eagle framework compiles business logic execution DAG into program primitives of the underlying stream infrastructure—for example, Apache Storm. + +**Alerting framework**: The Eagle alerting framework includes a stream metadata API, a policy engine provider API for extensibility, and a policy partitioner interface for scalability. + +**Machine-learning module**: Eagle provides capabilities to define user activity patterns or user profiles for Hadoop users based on the user behavior in the platform. The idea is to provide anomaly detection capability without setting hard thresholds in the system. The user profiles generated by our system are modeled using machine-learning algorithms and used for detection of anomalous user activities, where users’ activity pattern differs from their pattern history. Currently Eagle uses two algorithms for anomaly detection: Eigen-Value Decomposition and Density Estimation. The algorithms read data from HDFS audit logs, slice and dice data, and generate models for each user in the system. Once models are generated, Eagle uses the Storm framework for near-real-time anomaly detection to determine if current user activities are suspicious or not with respect to their model. The block diagram below shows the current pipeline for user profile training and online detection. + +### Eagle Service +**Policy Manager**: Eagle Policy Manager provides a UI and Restful API for users to define policies. The Eagle user interface makes it easy to manage policies with a few clicks, mark or import sensitivity metadata, perform HDFS or Hive resource browsing, access alert dashboards, etc. + +**Query Service**: Eagle provides a SQL-like service API to support comprehensive computation for huge sets of data—comprehensive filtering, aggregation, histogram, sorting, top, arithmetical expression, pagination, etc. Although Eagle supports HBase for data storage as a first-class citizen, a relational database is supported as well. For HBase storage, the Eagle query framework compiles a user-provided SQL-like query into HBase native filter objects, and then executes it through the HBase coprocessor on the fly. + diff --git a/community.md b/community.md index f941804..65b11df 100644 --- a/community.md +++ b/community.md @@ -8,7 +8,9 @@ permalink: /docs/community.html ### Issue Tracker -[https://github.com/eBay/Eagle/issues](https://github.com/eBay/Eagle/issues) +`Coming soon ... ` + +_Currently please use [https://github.com/eBay/Eagle/issues](https://github.com/eBay/Eagle/issues) for issue tracking_
    ### Events and Meetups @@ -24,8 +26,6 @@ permalink: /docs/community.html **Initial Commiters** -* [Adunuthula, Seshu](https://github.com/seshuad) -* [Arun Manoharan](https://github.com/armanoharan) * [Edward Zhang (张勇)](https://github.com/yonzhang) * [Hao Chen（陈浩）](https://github.com/haoch) * [Chaitali Gupta](https://github.com/chaitaligupta) @@ -33,6 +33,8 @@ permalink: /docs/community.html * [Jilin Jiang（将吉麟）](https://github.com/zombiej) * [Qingwen Zhao（赵晴雯）](https://github.com/qingwen220) * [Hemanth Dendukuri](https://github.com/hdendukuri) +* [Arun Manoharan](https://github.com/armanoharan) +* [Adunuthula, Seshu](https://github.com/seshuad)
    ### Project History @@ -41,4 +43,4 @@ permalink: /docs/community.html ### Credits -* Thanks [eBay Inc.](http://www.ebay.com) to donated this project to open source community, first announement at [eBay Techblog](#). +* Thanks [eBay Inc.](http://www.ebay.com) to donated this project to open source community, first announement at [eBay Techblog](http://www.ebaytechblog.com/2015/10/23/eagle-is-your-hadoop-data-secured/). diff --git a/css/font-awesome.min.css b/css/font-awesome.min.css new file mode 100644 index 0000000..ee4e978 --- /dev/null +++ b/css/font-awesome.min.css @@ -0,0 +1,4 @@ +/*! + * Font Awesome 4.4.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot?v=4.4.0');src:url('../fonts/fontawesome-webfont.eot?#iefix&v=4.4.0') format('embedded-opentype'),url('../fonts/fontawesome-webfont.woff2?v=4.4.0') 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diff --git a/css/styles.css b/css/styles.css index 7661e2e..ca69d1f 100755 --- a/css/styles.css +++ b/css/styles.css @@ -107,4 +107,17 @@ html[xmlns] .clearfix{display:block;} .page-main-content h1{ text-align: left +} + +.social-buttons > a{ + color:white; + margin:auto 2px; +} + +.social-buttons > a:hover{ + color: #126acb; +} + +.social-buttons > a > i{ + font-size: 1.2em; } \ No newline at end of file diff --git a/ecosystem.md b/ecosystem.md index 8126569..e2311ac 100644 --- a/ecosystem.md +++ b/ecosystem.md @@ -4,4 +4,16 @@ title: "Ecosystem" permalink: /docs/ecosystem.html --- -What's the unique value of eagle in the ecosystem? `TODO` \ No newline at end of file +### Eagle Core + +The core of eagle is the distributed real-time policy framework, including highly abstracted Streaming Programing API, CEP-based Policy Execution Engine, SQL-Like REST API based on HBase, Alert Deduplication, Alert Notification Framework and so on. + +### Extension + +Eagle supports pluggable policy execution engined like siddhi, machine learning and more, could support any types of data sources. + +### Integration +Eagle provides Ambari plugin for easily installation, can be easily intergrated into existing hadoop cluster. + +### User Interface +Eagle provide highly user-friendly UI for policy management \ No newline at end of file diff --git a/fonts/FontAwesome.otf b/fonts/FontAwesome.otf new file mode 100644 index 0000000..681bdd4 Binary files /dev/null and b/fonts/FontAwesome.otf differ diff --git a/fonts/fontawesome-webfont.eot b/fonts/fontawesome-webfont.eot new file mode 100644 index 0000000..a30335d Binary files /dev/null and b/fonts/fontawesome-webfont.eot differ diff --git a/fonts/fontawesome-webfont.svg b/fonts/fontawesome-webfont.svg new file mode 100644 index 0000000..6fd19ab --- /dev/null +++ b/fonts/fontawesome-webfont.svg @@ -0,0 +1,640 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/index.html @@ -293,16 +293,15 @@

    Discussion and Contribute

    Get help using Eagle or contribute to the project

    - Eagle is applying for apache incubation now, after accepted, we will mainly use apache mail list for general discussion. (Eagle Proposal)
    @@ -310,10 +309,9 @@

    Events and Meetups

    Learn more about Eagle from Conferences

    @@ -321,30 +319,32 @@

    Events and Meetups

    Social Network

    + +

    Learn latest updates about Eagle through:

    - - +
    +
    + + +
    + + + +
    + +
    + +
    +
    +
    + +
    @@ -421,5 +421,14 @@

    Social Network

    ga('create', 'UA-68929805-1', 'auto'); ga('send', 'pageview'); + +
    + diff --git a/introduction.md b/introduction.md index 5d88901..d42a401 100644 --- a/introduction.md +++ b/introduction.md @@ -17,7 +17,7 @@ permalink: /docs/index.html * **Open source**: Eagle is built ground up using open source standards and various products from the big data space. We decided to open source Eagle to help the community and looking forward for your feedback, collaboration and support. * **Extensibility**: Eagle is designed with extensibility in mind. You can integrate Eagle with existing data classification tools and monitoring tools easily. -### User Cases +### Use Cases Eagle data activity monitoring is currently deployed at eBay for monitoring the data access activities in a 2500 node hadoop cluster with plans of extending it to other hadoop clusters covering 10,000 nodes by end of this year. We have wide range of policies to detect and prevent data loss, data copy to unsecured location, sensitive data access from unauthorized zones etc. The flexibility of creating policies in eagle allows us to expand further and add more complex policies. diff --git a/terminology.md b/terminology.md index 53246de..0014a08 100644 --- a/terminology.md +++ b/terminology.md @@ -5,12 +5,12 @@ permalink: /docs/terminology.html --- Here are some terms we are using in Apache Eagle, please check them for your reference. -They are basic knowledge of Apache Eagle which also will help to well understand Eagle. +They are basic knowledge of Eagle which also will help to well understand Eagle. * **Site**: a site can be considered as a Hadoop environment. Different Hadoop clusters are called different sites. * **Policy**: a policy defines the rule to alert. Users can define their own policies based on the metadata of data sources * **Data source**: a data source is a monitoring target data. Currently Eagle only supports two kinds of data sources: Hive query log and HDFS audit log. * **Stream**: a stream is the streaming data from a data source. Each data source has its own stream. * **Data activity monitoring**: Data activity monitoring is to monitor the data from each data source and to alert according the policy defined by users. -* **User profile**: +* **User profile**: a user profile is the historical activity model generated using machine learning algorithm which could be used for real-time online abnormal detection. * **Data classification**: data classification provides the ability to classify different data sources with different levels of sensitivity. diff --git a/user-profile-ml.md b/user-profile-ml.md new file mode 100644 index 0000000..128d11b --- /dev/null +++ b/user-profile-ml.md @@ -0,0 +1,23 @@ +--- +layout: doc +title: "User Profile Machine Learning" +permalink: /docs/user-profile-ml.html +--- + +Eagle provides capabilities to define user activity patterns or user profiles for Hadoop users based on the user behavior in the platform. The idea is to provide anomaly detection capability without setting hard thresholds in the system. The user profiles generated by our system are modeled using machine-learning algorithms and used for detection of anomalous user activities, where users’ activity pattern differs from their pattern history. Currently Eagle uses two algorithms for anomaly detection: Eigen-Value Decomposition and Density Estimation. The algorithms read data from HDFS audit logs, slice and dice data, and generate models for each user in the system. Once models are generated, Eagle uses the Storm framework for near-real-time anomaly detection to determine if current user activities are suspicious or not with respect to their model. The block diagram below shows the current pipeline for user profile training and online detection. + +![](/images/docs/userprofile-arch.png) + +Eagle online anomaly detection uses the Eagle policy framework, and the user profile is defined as one of the policies in the system. The user profile policy is evaluated by a machine-learning evaluator extended from the Eagle policy evaluator. Policy definition includes the features that are needed for anomaly detection (same as the ones used for training purposes). + +A scheduler runs a Spark-based offline training program (to generate user profiles or models) at a configurable time interval; currently, the training program generates new models once every month. + +The following are some details on the algorithms. + +* **Density Estimation**: In this algorithm, the idea is to evaluate, for each user, a probability density function from the observed training data sample. We mean-normalize a training dataset for each feature. Normalization allows datasets to be on the same scale. In our probability density estimation, we use a Gaussian distribution function as the method for computing probability density. Features are conditionally independent of one another; therefore, the final Gaussian probability density can be computed by factorizing each feature’s probability density. During the online detection phase, we compute the probability of a user’s activity. If the probability of the user performing the activity is below threshold (determined from the training program, using a method called Mathews Correlation Coefficient), we signal anomaly alerts. +* **Eigen-Value Decomposition**: Our goal in user profile generation is to find interesting behavioral patterns for users. One way to achieve that goal is to consider a combination of features and see how each one influences the others. When the data volume is large, which is generally the case for us, abnormal patterns among features may go unnoticed due to the huge number of normal patterns. As normal behavioral patterns can lie within very low-dimensional subspace, we can potentially reduce the dimension of the dataset to better understand the user behavior pattern. This method also reduces noise, if any, in the training dataset. Based on the amount of variance of the data we maintain for a user, which is usually 95% for our case, we seek to find the number of principal components k that represents 95% variance. We consider first k principal components as normal subspace for the user. The remaining (n-k) principal components are considered as abnormal subspace. + +During online anomaly detection, if the user behavior lies near normal subspace, we consider the behavior to be normal. On the other hand, if the user behavior lies near the abnormal subspace, we raise an alarm as we believe usual user behavior should generally fall within normal subspace. We use the Euclidian distance method to compute whether a user’s current activity is near normal or abnormal subspace. + +![](/images/docs/userprofile-model.png) +