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<ul class="nav navbar-nav navbar-right" id="top-menu"> + <li><a class="menu" href="/#home_page">HOME</a></li> + <li><a class="menu" href="/docs/">DOCS</a></li> + <li><a class="menu" href="/#about_page">ABOUT</a></li> + <li><a class="menu" href="/#diagram_page">ARCHITECTURE</a></li> + <li><a class="menu" href="/#modules_page">MODULES</a></li> + <li><a class="menu" href="/#usecase_page">USE CASES</a></li> + <li> + </li> + </ul> --> + </div> + </div> + <!-- /.container-fluid --> + </nav> + </div> + </div> + </div> + <div class="headerimage"> + <div class="flexslider"> + <ul class="slides"> + <li><img src="/images/slider/4.jpg" alt="Slide 1"></li> + </ul> + </div> + </div> + <div class="particles" style="height:40%"> </div><!---particles--> + <div class="slider-caption" style="top:80px;"> + <div class="homewrapper"> + <div class="hometitle"> + <a href="/"> + <img src="/images/feather.png" height="80px"> + </a> + </div> + <div class="hometext"> + <h2>Analyze Big Data Platforms For Security and Performance</h2> + <div class="social-buttons"> + <a href="https://github.com/apache/eagle";><i class="fa fa-github"></i></a> + <a href="http://twitter.com/TheApacheEagle";><i class="fa fa-twitter"></i></a> + <a href="https://www.facebook.com/TheApacheEagle/";><i class="fa fa-facebook"></i></a> + <a href="#"><i class="fa fa-weixin"></i></a> + <!-- <a href="https://www.weibo.com/ApacheEagle/";><i class="fa fa-weibo"></i></a> --> + </div> + </div> + </div> + </div> +</div> +<!-- header end --> +<div class="container-fluid page-content"> + <div class="row"> + <div class="col-md-10 col-md-offset-1"> + <!-- sidebar --> + <div class="col-xs-6 col-sm-3" id="sidebar" role="navigation"> + <ul class="nav" id="adminnav"> + + <li class="heading">Getting Started</li> + + <li class="sidenavli "><a href="/docs/index.html" data-permalink="/docs/user-profile-ml.html" id="">Introduction</a></li> + + <li class="sidenavli "><a href="/docs/usecases.html" data-permalink="/docs/user-profile-ml.html" id="">Use Cases</a></li> + + <li class="sidenavli "><a href="/docs/terminology.html" data-permalink="/docs/user-profile-ml.html" id="">Terminology</a></li> + + <li class="sidenavli "><a href="/docs/ecosystem.html" data-permalink="/docs/user-profile-ml.html" id="">Ecosystem</a></li> + + <li class="sidenavli "><a href="/docs/community.html" data-permalink="/docs/user-profile-ml.html" id="">Community</a></li> + + <li class="sidenavli "><a href="/docs/FAQ.html" data-permalink="/docs/user-profile-ml.html" id="">FAQ</a></li> + + <li class="divider"></li> + + <li class="heading">Documentations</li> + + <li class="sidenavli "><a href="/docs/latest/" data-permalink="/docs/user-profile-ml.html" id="">Latest version (v0.5.0)</a></li> + + <li class="divider"></li> + + <li class="heading">Download</li> + + <li class="sidenavli "><a href="/docs/download-latest.html" data-permalink="/docs/user-profile-ml.html" id="">Latest version (v0.5.0)</a></li> + + <li class="sidenavli "><a href="/docs/download.html" data-permalink="/docs/user-profile-ml.html" id="">Archived</a></li> + + <li class="divider"></li> + + <li class="heading">Supplement</li> + + <li class="sidenavli "><a href="/docs/security.html" data-permalink="/docs/user-profile-ml.html" id="">Security</a></li> + + <li class="divider"></li> + + <li class="sidenavli"> + <a href="mailto:[email protected]"; target="_blank">Need Help?</a> + </li> + </ul> + </div> + <div class="col-xs-6 col-sm-9 page-main-content" style="margin-left: -15px" id="loadcontent"> + <h1 class="page-header" style="margin-top: 0px">User Profile Machine Learning</h1> + <p>Apache Eagle (called Eagle in the following) provides capabilities to define user activity patterns or user profiles for Apache 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 usersâ 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 Apache 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 traini ng and online detection.</p> + +<p><img src="/images/docs/userprofile-arch.png" alt="" /></p> + +<p>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).</p> + +<p>A scheduler runs a Apache 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.</p> + +<p>The following are some details on the algorithms.</p> + +<ul> + <li><strong>Density Estimation</strong>: 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 featureâs probability density. During the online detection phase, we compute the probability of a userâs 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.</li> + <li><strong>Eigen-Value Decomposition</strong>: 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.</ li> +</ul> + +<p>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 userâs current activity is near normal or abnormal subspace.</p> + +<p><img src="/images/docs/userprofile-model.png" alt="" /></p> + + </div><!--end of loadcontent--> + </div> + <!--end of centered content--> + </div> +</div> +<!--end of container--> + +<!-- footer start --> +<div class="footerwrapper"> + <div class="container"> + <div class="row"> + <div class="col-md-12"><div style="margin-left:auto; margin-right:auto; text-align:center;font-size: 12px"> +<div> +</div> +<div> +<a href="http://www.apache.org";> +<img id="asf-logo" alt="Apache Software Foundation" src="/images/apache-logo-small.gif"> +</a> +</div> +<div> +Copyright © 2015 <a href="http://www.apache.org";>The Apache Software Foundation</a>, Licensed under the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. +</div> +<div> +Apache Eagle, Eagle, Apache Hadoop, Hadoop, Apache HBase, HBase, Apache Hive, Hive, Apache Ambari, Ambari, Apache Spark, Spark, Apache Kafka, Kafka, Apache Storm, Storm, Apache Maven, Maven, Apache Tomcat, Tomcat, Apache Derby, Derby, Apache Cassandra, Cassandra, Apache ZooKeeper, ZooKeeper, Apache, the Apache feather logo, and the Apache project logo are trademarks of The Apache Software Foundation. +</div> + </div></div> + </div> + </div> +</div> +<!-- footer end --> + +<!-- JavaScripts --> +<script src="/js/jquery-1.11.1.min.js"></script> +<script src="/js/jquery.singlePageNav.js"></script> +<script src="/js/jquery.flexslider.js"></script> +<script src="/js/modernizr.min.js"></script> +<script src="/js/svg.js"></script> +<script> + /************** FlexSlider *********************/ + $('.flexslider').flexslider({ + animation: "fade", + directionNav: false + }); +</script> + +<script> + /************** FlexSlider *********************/ + $('.flexslider').flexslider({ + animation: "fade", + directionNav: false + }); +</script> +</body> +</html>
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source + .flatMap(new AuditLogTransformer()) // transform event + .groupBy(Arrays.asList(0)) // group by 1st field + .flatMap(new UserProfileAggregatorExecutor()); // aggregate one-hour data by user + .alertWithConsumer(âuserActivityâ,âuserProfileExecutorâ) // ML policy evaluation +env.execute(); // execute stream processing and alert +</code></pre> + +<p><strong>åè¦æ¡æ¶ï¼Alerting Frameworkï¼Eagle</strong>åè¦æ¡æ¶ç±æµå æ°æ®APIãçç¥å¼ææå¡æä¾APIãçç¥Partitioner API 以åé¢è¦å»éæ¡æ¶çç»æ:</p> + +<ul> + <li><strong>æµå æ°æ®API</strong> å 许ç¨æ·å£°æäºä»¶çSchemaï¼å æ¬äºä»¶ç±åªäºå±æ§ææãæ¯ä¸ªå±æ§çç±»åï¼ä»¥åå½ç¨æ·é ç½®çç¥æ¶å¦ä½å¨è¿è¡æ¶å¨æ解æå±æ§çå¼çã</li> + <li><strong>çç¥å¼ææå¡æä¾API</strong> å 许å¼åè å¾å®¹æå°ä»¥æ件çå½¢å¼æ©å±æ°ççç¥å¼æãWSO2 Siddhi CEP å¼ææ¯Eagle ä¼å é»è®¤æ¯æççç¥å¼æï¼åæ¶æºå¨å¦ä¹ ç®æ³ä¹å¯ä½ä¸ºå¦ä¸ç§çç¥å¼ææ§è¡ã</li> + <li> + <p><strong>æ©å±æ§</strong> Eagleççç¥å¼ææå¡æä¾APIå è®¸ä½ æå ¥æ°ççç¥å¼æ</p> + + <pre><code> public interface PolicyEvaluatorServiceProvider { + public String getPolicyType(); // literal string to identify one type of policy + public Class&lt;? extends PolicyEvaluator&gt; getPolicyEvaluator(); // get policy evaluator implementation + public List&lt;Module&gt; getBindingModules(); // policy text with json format to object mapping + } + public interface PolicyEvaluator { + public void evaluate(ValuesArray input) throws Exception; // evaluate input event + public void onPolicyUpdate(AlertDefinitionAPIEntity newAlertDef); // invoked when policy is updated + public void onPolicyDelete(); // invoked when policy is deleted + } +</code></pre> + </li> + <li><strong>çç¥Partitioner API</strong> å 许çç¥å¨ä¸åçç©çèç¹ä¸å¹¶è¡æ§è¡ãä¹å è®¸ä½ èªå®ä¹çç¥Partitionerç±»ãè¿äºåè½ä½¿å¾çç¥åäºä»¶å®å ¨ä»¥åå¸å¼çæ¹å¼æ§è¡ã</li> + <li> + <p><strong>å¯ä¼¸ç¼©æ§</strong> Eagle éè¿æ¯æçç¥çååºæ¥å£æ¥å®ç°å¤§éççç¥å¯ä¼¸ç¼©å¹¶åå°è¿è¡</p> + + <pre><code> public interface PolicyPartitioner extends Serializable { + int partition(int numTotalPartitions, String policyType, String policyId); // method to distribute policies + } +</code></pre> + + <p><img src="/images/posts/policy-partition.png" alt="" /></p> + + <blockquote> + <p>å¯ä¼¸ç¼©çEagleçç¥æ§è¡æ¡æ¶</p> + </blockquote> + </li> +</ul> + +<p><strong>æºå¨å¦ä¹ 模å:</strong> +Eagle æ¯ææ ¹æ®ç¨æ·å¨Hadoopå¹³å°ä¸åå²ä½¿ç¨è¡ä¸ºä¹ æ¯æ¥å®ä¹è¡ä¸ºæ¨¡å¼æç¨æ·Profileçè½åãæ¥æäºè¿ä¸ªåè½ï¼ä¸éè¦å¨ç³»ç»ä¸é¢å 设置åºå®ä¸´çå¼çæ åµä¸ï¼ä¹å¯ä»¥å®ç°æºè½å°æ£æµåºå¼å¸¸çè¡ä¸ºãEagleä¸ç¨æ·Profileæ¯éè¿æºå¨å¦ä¹ ç®æ³çæï¼ç¨äºå¨ç¨æ·å½åå®æ¶è¡ä¸ºæ¨¡å¼ä¸å ¶å¯¹åºçåå²æ¨¡å模å¼åå¨ä¸å®ç¨åº¦çå·®å¼æ¶è¯å«ç¨æ·è¡ä¸ºæ¯å¦ä¸ºå¼å¸¸ãç®åï¼Eagle å ç½®æä¾ä»¥ä¸ä¸¤ç§ç®æ³æ¥æ£æµå¼å¸¸ï¼åå«ä¸ºç¹å¾å¼å解ï¼Eigen-Value Decompositionï¼å å¯åº¦ä¼°è®¡ï¼Density Estimationï¼ãè¿äºç®æ³ä»HDFS 审计æ¥å¿ä¸è¯»åæ°æ®ï¼å¯¹æ°æ®è¿è¡åå²ã审æ¥ã交ååæï¼å¨ææ§å°ä¸ºæ¯ä¸ªç¨æ·ä¾æ¬¡å建Profile è¡ä¸ºæ¨¡åãä¸æ¦æ¨¡åçæï¼Eagleçå®æ¶æµçç¥å¼æè½å¤è¿ä¹å®æ¶å°è¯å«åºå¼å¸¸ï¼å辨å½åç¨æ·çè¡ä¸ºå¯ççæè ä¸ä»ä»¬çåå²è¡ä¸ºæ¨¡åä¸ç¸ç¬¦ã</p> + +<p>ä¸å¾ç®åæè¿°äºç®åEagleä¸ç¨æ·Profileç离线è®ç»å»ºæ¨¡åå¨çº¿å®æ¶çæµçæ°æ®æµï¼</p> + +<p><img 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<p>åä¸äºä»¶æ§è¡çç¥ï¼ç¨æ·è®¿é®Hiveä¸çæææ°æ®åï¼</p> + + <pre><code> from hiveAccessLogStream[sensitivityType=='PHONE_NUMBER'] select * insert into outputStream; +</code></pre> + </li> + <li> + <p>åºäºçªå£ççç¥ï¼ç¨æ·å¨10åéå 访é®ç®å½ /tmp/private å¤ä½ 5次ï¼</p> + + <pre><code> hdfsAuditLogEventStream[(src == '/tmp/private')]#window.externalTime(timestamp,10 min) select user, count(timestamp) as aggValue group by user having aggValue &gt;= 5 insert into outputStream; +</code></pre> + </li> +</ul> + +<p><strong>æ¥è¯¢æå¡ï¼Query Serviceï¼</strong> Eagle æä¾ç±»SQLçREST APIç¨æ¥å®ç°é对海éæ°æ®éç综å计ç®ãæ¥è¯¢ååæçè½åï¼æ¯æä¾å¦è¿æ»¤ãèåãç´æ¹è¿ç®ãæåºãtopãç®æ¯è¡¨è¾¾å¼ä»¥åå页çãEagleä¼å æ¯æHBase ä½ä¸ºå ¶é»è®¤æ°æ®åå¨ï¼ä½æ¯åæ¶ä¹æ¯æåºJDBCçå ³ç³»åæ°æ®åºãç¹å«æ¯å½éæ©ä»¥HBaseä½ä¸ºåå¨æ¶ï¼Eagle便åçæ¥æäºHBaseåå¨åæ¥è¯¢æµ·éçæ§æ°æ®çè½åï¼Eagle æ¥è¯¢æ¡æ¶ä¼å°ç¨æ·æä¾çç±»SQLæ¥è¯¢è¯æ³æç»ç¼è¯æ为HBase åççFilter 对象ï¼å¹¶æ¯æéè¿HBase Coprocessorè¿ä¸æ¥æåååºé度ã</p> + +<pre><code>query=AlertDefinitionService[@dataSource="hiveQueryLog"]{@policyDef}&amp;pageSize=100000 +</code></pre> + +<h2 id="eagleebay">Eagleå¨eBayç使ç¨åºæ¯</h2> +<p>ç®åï¼Eagleçæ°æ®è¡ä¸ºçæ§ç³»ç»å·²ç»é¨ç½²å°ä¸ä¸ªæ¥æ2500å¤ä¸ªèç¹çHadoopé群ä¹ä¸ï¼ç¨ä»¥ä¿æ¤æ°ç¾PBæ°æ®çå®å ¨ï¼å¹¶æ£è®¡åäºä»å¹´å¹´åºä¹åæ©å±å°å ¶ä»ä¸å个Hadoopé群ä¸ï¼ä»èè¦çeBay ææ主è¦Hadoopç10000å¤å°èç¹ãå¨æ们çç产ç¯å¢ä¸ï¼æ们已é对HDFSãHive çé群ä¸çæ°æ®é ç½®äºä¸äºåºç¡çå®å ¨çç¥ï¼å¹¶å°äºå¹´åºä¹åä¸æå¼å ¥æ´å¤ççç¥ï¼ä»¥ç¡®ä¿éè¦æ°æ®çç»å¯¹å®å ¨ãç®åï¼Eagleççç¥æ¶µçå¤ç§æ¨¡å¼ï¼å æ¬ä»è®¿é®æ¨¡å¼ãé¢ç¹è®¿é®æ°æ®éï¼é¢å®ä¹æ¥è¯¢ç±»åãHive 表ååãHBase 表以ååºäºæºå¨å¦ä¹ 模åçæçç¨æ·Profileç¸å ³çææçç¥çãåæ¶ï¼æ们ä¹æ广æ³ççç¥æ¥é²æ¢æ°æ®ç丢失ãæ°æ®è¢«æ·è´å°ä¸å®å ¨å°ç¹ãæææ°æ®è¢«æªææåºå访é®çãEagleçç¥å®ä¹ä¸æ大ççµæ´»æ§åæ©å±æ§ä½¿å¾æ们æªæ¥å¯ä»¥è½»æå°ç»§ç»æ©å±æ´å¤æ´å¤æççç¥ä»¥æ¯ææ´å¤å¤å åçç¨ä¾åºæ¯ã</p> + +<h2 id="section-1">åç»è®¡å</h2> +<p>è¿å»ä¸¤å¹´ä¸ï¼å¨eBay é¤äºè¢«ç¨äºæ°æ®è¡ä¸ºçæ§ä»¥å¤ï¼Eagle æ ¸å¿æ¡æ¶è¿è¢«å¹¿æ³ç¨äºçæ§èç¹å¥åº·ç¶åµãHadoopåºç¨æ§è½ææ ãHadoop æ 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href="https://github.com/sunlibin">åç«æ</a>ï¼Apache Eagle CommitteråPMCï¼eBay 软件工ç¨å¸</li> + <li><a href="https://github.com/zombiej">èåéº</a>ï¼Apache Eagle CommitteråPMCï¼eBay 软件工ç¨å¸</li> + <li><a href="https://github.com/qingwen220">èµµæ´é¯</a>ï¼Apache Eagle CommitteråPMCï¼eBay 软件工ç¨å¸</li> +</ul> + +<p>eBay åæå¹³å°åºç¡æ¶æé¨ï¼Analytics Data Infrastructureï¼æ¯eBayçå ¨çæ°æ®ååæåºç¡æ¶æé¨é¨ï¼è´è´£eBayå¨æ°æ®åºãæ°æ®ä»åºãHadoopãåå¡æºè½ä»¥åæºå¨å¦ä¹ çå个æ°æ®å¹³å°å¼åã管çç,æ¯æeBayå ¨çåé¨é¨è¿ç¨é«ç«¯çæ°æ®åæ解å³æ¹æ¡ä½åºåæ¶ææçä½ä¸å³çï¼ä¸ºéå¸å ¨ççä¸å¡ç¨æ·æä¾æ°æ®åæ解å³æ¹æ¡ã</p> + +<h2 id="section-3">åèèµæ</h2> + +<ul> + <li>Apache Eagle ææ¡£ï¼<a href="http://goeagle.io">http://goeagle.io</a></li> + <li>Apache Eagle æºç ï¼<a href="http://github.com/apache/incubator-eagle">http://github.com/apache/incubator-eagle</a></li> + <li>Apache Eagle 项ç®ï¼<a href="http://incubator.apache.org/projects/eagle.html">http://incubator.apache.org/projects/eagle.html</a></li> +</ul> + +<h2 id="section-4">å¼ç¨é¾æ¥</h2> +<ul> + <li><strong>CSDN</strong>: <a href="http://www.csdn.net/article/2015-10-29/2826076">http://www.csdn.net/article/2015-10-29/2826076</a></li> + <li><strong>OSCHINA</strong>: <a href="http://www.oschina.net/news/67515/apache-eagle">http://www.oschina.net/news/67515/apache-eagle</a></li> + <li><strong>China Hadoop Summit</strong>: <a href="http://mp.weixin.qq.com/s?__biz=MzA4MTkyODIzMA==&amp;mid=400298495&amp;idx=1&amp;sn=954031ba8065481c31a3464e2c8a26a5&amp;scene=1&amp;srcid=1102zgGQzedckCmNrfRwounA&amp;uin=MjYyNzgwNDQwMA%3D%3D&amp;key=04dce534b3b035efe14d53fcf6e7062a63179003551e59fad5cf8584703fcaa38779cc4c93cbf931c25f6b34cb2d7653&amp;devicetype=iMac+MacBookPro11%2C3+OSX+OSX+10.10.5+build(14F1021)&amp;version=11020201&amp;lang=en&amp;pass_ticket=TC%2Bod2ZeFnhmci%2Bi4%2BxTTVD6moUrNFX8RXppzoQSa%2BXO3C7evUDs6njeYbsYyCFD">http://mp.weixin.qq.com/s?â¦</a></li> + <li><strong>Apache Kylin</strong>: <a href="http://mp.weixin.qq.com/s?__biz=MzAwODE3ODU5MA==&amp;mid=400287781&amp;idx=1&amp;sn=343b2b29a37f8ed53a7ecb0465faf515&amp;scene=0&amp;uin=MjYyNzgwNDQwMA%3D%3D&amp;key=04dce534b3b035ef73f964362ac4c43d452ab1b208eb357c488dfcd7d69209e060cfe01e9b146752517d2096f6751370&amp;devicetype=iMac+MacBookPro11%2C3+OSX+OSX+10.10.5+build(14F1021)&amp;version=11020201&amp;lang=en&amp;pass_ticket=TC%2Bod2ZeFnhmci%2Bi4%2BxTTVD6moUrNFX8RXppzoQSa%2BXO3C7evUDs6njeYbsYyCFD">http://mp.weixin.qq.com/s?â¦</a></li> +</ul> + +<hr /> + +<p><em>æ¬ææ¥èªApache Eagleç½ç«ï¼<a href="http://goeagle.io">http://goeagle.io</a>ï¼è½¬è½½è¯·æ³¨æåºå¤åæ¥æºã</em></p> +</description> + <pubDate>Tue, 27 Oct 2015 03:24:33 +0800</pubDate> + <link>http://goeagle.io/post/2015/10/27/apache-eagle-announce-cn.html</link> + <guid isPermaLink="true">http://goeagle.io/post/2015/10/27/apache-eagle-announce-cn.html</guid> + + + <category>post</category> + + </item> + + </channel> +</rss> 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