Category Archives: Security Learning

Punching Hard – QRadar Security Intelligence Platform


QRadarLogo

Off late, at Infosecnirvana, we have been looking beyond ArcSight Enterprise Security Platform (ESP) to see if there are any other SIEM products that either challenge or match up or exceed the capability of ArcSight ESP. One of the products that has caught our attention in recent times is the IBM acquisition – Q1 Labs offering – QRadar Security Intelligence Platform. IBM completed this buy in 2011 and jump started their Security Systems Division providing a platform to compete against HP who jump started their Enterprise Security Products group with the buying of ArcSight in 2010. Both of them are competing hard in the market place and are vying for the top spot as evidenced in numerous SIEM vendor analysis and reports.

Gartner reports are something that every company looks before investing in a SIEM solution. The interesting thing about QRadar that caught our attention is how consistently it has climbed the ladder of the SIEM Leaders Quadrant. Lets take a look at the last 3 years of the Gartner Magic Q to get an idea of the rapid climb of QRadar against ArcSight.

Picture1

Looking at the graph more closely, even McAfee Nitro and Splunk are catching up in the leaders Q. However, in this post we will concentrate on Q1 Labs QRadar only as they are by and large the biggest threat to ArcSight in terms of technology and capability, not to mention Market share.

First things First:

The QRadar Integrated Security Solutions (QRadar) Platform is an integrated set of products for collecting, analysing, and managing enterprise Security Event information. The various components that are part of this Platform are:

  • QRadar Log Manager – log management solution for Event log collection & storage.
  • QRadar SIEM – Correlation engine
  • QRadar VM – Vulnerability scanner and management tool set available to integrate Event data to Vulnerability data. This provides on demand scans, rescans and vulnerability tracking.
  • QRadar QFlowNetwork Behaviour Analysis & Anomaly detection using network flow data. QFlow provides payload information (up to Layer 7) in every detected event which is a great value addition to Netflow data. 
  • QRadar vFlow – Application Layer monitoring for both Physical & Virtual environment.

Key Strengths of QRadar: Few of the things that blew us away when we played around with IBM QRadar was:

  • Easy Setup – It was a breeze to install the product. There are very few or no moving parts in the installation process. The console is also Web based and is a full functional console. From a deployment and operations perspective, this comes across as a super easy, super quick solution to SIEM needs.
  • Value Out of the Box – QRadar comes packed with a lot of content Out of the box to get up and running. The Dashboards are already built for you, more than 1500 reports are waiting for you to just click and run, rules are categorized nicely under various Threat sections and immediately start firing “Offenses” (Correlation rule triggers are called so in IBM world), Network Flow and Packet data are available instantly under the same unified console when triggers are analysed and so on and so forth. We have never seen such quick turnaround times with any other SIEM product in recent times.
  • Completely Replicated Architecture – Full replication is available in the product and can be enabled with a click. This is something which we were really impressed with. In major organisations, this is non-negotiable and such a easy set up really builds up a story.

Key Weakness of the Product: Now being ArcSight users for several years now, this section is something which is right down our alley. Some of the key weakness we saw with the product are:

  • Scale: In spite of all the ease of set up and value Out of the box, when compared against ArcSight, scaling up with multiple tiers is a problem. One of the caveats we see here is that QRadar is an appliance based model. You can have several collector appliances, but to query them you can have only only Manager or Console Appliance. This will severely impact the scalability in a multi-tier set up.
  • Multi-Tenancy: ArcSight has always been best suited for a Managed service implementation with its Customer tagging, zoning and overall multi-tenancy architecture. However, this is a big problem when it comes to QRadar. They don’t have such a capability today. However, we believe their product road map does talk about such features in the future, but we will have to bite our nails in anticipation.
  • Customization: One of the things which propelled ArcSight to land major defence and government contracts was its capability to customize almost everything except the core source code. When creating Content like Use Cases, Rules, Reports, Third party integration etc. this customization capability comes in handy. Such customization & flexibility is seldom seen in any SIEM product out there. QRadar offers some of these customization, but the moment you take it along that route, you will be disappointed on what it lets you do – Read NO API.
  • Workflow: Other impressive thing about ArcSight is its wonderful content management workflow. It has a full blow case management workflow, event handling workflow, Use Cases workflow etc. whereas QRadar falls short as it does not have any such powerful workflow capabilities. Hopefully IBM will address it in future product releases.

Overall Comparison with ArcSight: ArcSight ESP by far has been the oldest and supposedly the most mature SIEM offering in the market but honestly they are losing ground because, they have not been seriously challenged so far. QRadar does that exactly. Based on the key Strengths and Weaknesses of the product, you should have got an idea of where the product stands.

  • Most of the customers would love to get QRadar in their environment just for the ease of set up and Out of the box value. ArcSight is still a pain to set up and generate value. Most of the implementations of ArcSight have failed for the simple reason – Complexity
  • QRadar put a lot of emphasis on Network security based monitoring approach, where as ArcSight takes an Identity based Security monitoring approach. This is an interesting because the Cyber security world is still split about what is key – “Identity based or Network Security based”. In our humble opinion, a mix of both is what really works.

In Conclusion: QRadar definitely is a wonderful product and a worthy competitor to ArcSight as the battle for the top prize plays out. As technology enthusiasts, we are eager to see how the market plays out, but one thing is for sure

“QRadar Security Intelligence Platform is definitely Punching Hard”.

There you have it!!! Let me know what you guys think about these two products and which one do you prefer and why? Comment on below.

Big Data – What you need to know?


Big Data is the buzzword in IT Circle nowadays. The major reason for this is the exploding “Netizen” base. Today Everything is happening Online and Online Data is estimated in zettabytes. The wealth of information one can carve from Online data is undeniably attractive for several organizations for marketing and sales. Organizations like Google, Yahoo, Facebook, Amazon etc process several Petabytes of data on a daily basis. Many more organizations are moving towards being able to collect, store and make sense of data in the Internet to further their interests.That is where “Big Data” has caught the imagination of people around the world. But What is Big Data and How can I jump into this bandwagon. Fret not, for in the blog post, you are going to find all about it.  The structure of this blog will be typical of a What you need to know? series posted at Infosecnirvana.com. So lets get started!!!

What is Data?
Data is anything that provides value in a structured or unstructured format. It is the lowest level of abstraction in Computing terms because after this, it is binary digits only. Data is typically stored in File Systems

Introducing File Systems
File Systems are the basis of storing and accessing data from a hardware device. It is nothing but an abstraction layer of software/firmware that gives you the capability to store data in a structured format, remember the structure and when queried, help retrieve it as quickly as possible. There are 2 major and common types of File Systems – Disk Based (local access) and Network Based (remote access). To give a simple example, FAT is a Windows Disk based File System wheres NFS is a Network based File System.

Even though both the file systems continue to dominate IT space, more and more relevance is given to Network based File Systems for obvious reasons like Distributed Data storage, redundancy, fault tolerance capabilities etc. This is the basis of “Big Data Tools and Technologies”.

Introducing DFS
Distributed File Systems are Network based File Systems that allow data to be shared across multiple machines across multiple networks. This makes it possible for multiple users on multiple machines to share files and storage resources. The client machines don’t have direct access to the Storage disk itself (as in a Disk based file system), but are able to interact with the Data using a File System protocol. One classic example of DFS is Microsoft SMB where All Windows machines are SMB Clients and access a common SMB Share on the File Server. But SMB suffers from issues pertaining to scalability and fault tolerance. This is where systems like Google File System – GFS (Google uses this in their search engine) and Hadoop Distributed File System – HDFS (Yahoo and others) come into prominence. What these File Systems do is provide a mechanism to effectively manage big data collection, storage and processing across multiple machine nodes.

Introducing HDFS:

Hadoop Distributed File Systems or shortly HDFS is similar to the other DFS file systems talked above, however it is significantly different as well. HDFS can be deployed on Commodity Hardware, is Highly Fault Tolerant and is very capable of handling large data sets. Originally HDFS was developed as part of the Apache NUTCH Project for an alternate Search Engine akin to Google. Some of the most prominent software players for HDFS are “Apache Hadoop”, “Greenplum”, Cloudera etc.

In this post, we will be looking at Log Collection and Management using the Hadoop Platform.

APACHE Hadoop: The Apache Hadoop architecture in a Nutshell consists of the following components:

  • HDFS is a Master Slave Architecture
  • Master Server is called a NameNode
  • Slave Servers are called DataNodes
  • Underlying Data Replication across Nodes
  • Interface Language – Java

Installing Hadoop: Installation of Apache Hadoop is not a very easy task, but at the same time it is not too complex either. Understanding of the Hardware Requirements, Operating System Requirements and Java Programming Language can help you install Apache Hadoop without any issues. Installing Hadoop can be either a Single Node Installation or a Cluster Installation. For this post, we will look at only Single Node Installation steps:

  1. Install Oracle Java on your machine – Ubuntu
  2. Install OpenSSH Server
  3. Create a Hadoop Group and Hadoop User and set Key Based Login for SSH
  4. Download the Latest Distribution of Hadoop from http://www.apache.org/dyn/closer.cgi
  5. Installation is just extracting the Hadoop files into a folder and editing some property files
  6. Provide the location for the JAVA home in the following file location- hadoop/conf/hadoop-env.sh
  7. Create a working folder in Hadoop User Home Directory /home//tmp
  8. Add the relevant details about the host and the home directory following configuration elements in /hadoop/conf/core-site.xml
    conf/core-site.xml —>
    
    hadoop.tmp.dir
    /home//tmp
    A base for other temporary directories.
    
    fs.default.name
    hdfs://localhost:54310
    The name of the default file system. A URI whose
    scheme and authority determine the FileSystem implementation. The
    uri’s scheme determines the config property (fs.SCHEME.impl) naming
    the FileSystem implementation class. The uri’s authority is used to
    determine the host, port, etc. for a filesystem.
  9. Then we need to edit the hadoop/conf/mapred-site.xml using a text editor and add the following configuration values (like core-site.xml)
    conf/mapred-site.xml —>
    
    mapred.job.tracker
    localhost:54311
    The host and port that the MapReduce job tracker runs
    at. If “local”, then jobs are run in-process as a single map
    and reduce task.
  10. Open hadoop/conf/hdfs-site.xml using a text editor and add the following configurations:
    conf/hdfs-site.xml —>
    
    dfs.replication
    1
    Default block replication.
    The actual number of replications can be specified when the file is created.
    The default is used if replication is not specified in create time.
  11. Before running the Hadoop Installation, the most important step is to format the NameNode or the Master Server. This is critical because, Without the NameNode, the DataNodes will not be setup. In a Single Node Installation, NameNode and DataNodes will reside on the same host, where as in Cluster Installation, NameNodes and DataNodes will reside on different hosts. In order to format the NameNode using Hadoop commands, Run the following command – /hadoop/bin/hadoop namenode -format
  12. In order to start the Hadoop Instance, from hadoop/bin run ./start-dfs.sh and Running the commands will start up Hadoop and when you query the Java Process, you should be able to see the following components of Hadoop Running:
    NameNode
    DataNode
    SecondaryNameNode
    JobTracker
    TaskTracker
  13. If you have successfully completed till this, then you now have a Hadoop Single Node Instance running on your machine.

Getting Data in/out of Hadoop:

Once the installation is completed, the next thing we need to worry about is getting data in and out of Hadoop File System. Typically in order to get the data into the system, we need a API interface into HDFS. This typically is a JAVA or HTTP API. Tools like FluentD, Flume etc help in getting data in and out of Hadoop. Both the tools have plugins for receiving HTTP data, Streaming data and Syslog Data as well.

MapReduce: Hadoop and Big data discussions are incomplete without talking about MapReduce. MapReduce is a software policy framework that maps Input data based on a map file and outputs data in key value pairs. These are two different jobs when it comes to actual processing. One is the Map Task that splits the data into smaller chunks and there is the Reduce Job that generates a Key Value combination for each of the smaller data chunks. This framework is the powerhouse for Hadoop because, this is built with parallelism in mind. Map Tasks and Reduce Tasks can both be run parallel on several machines without compromising on speed, cpu and memory resources. The NameNode is the central master that tracks the Maps and the Jobs where as the DataNodes are just providing processing resource.

Finally, Using Hadoop: Now that we know what drives Hadoop and how to get Hadoop installed, the easiest thing would be to start using them. Several examples for MapReduce jobs using Java are available to aid in learning. There are several related projects running to make the Hadoop Ecosystem more scalable and mature. Some of them are:

  • HBase, a Bigtable-like structured storage system for Hadoop HDFS
  • Apache Pig is a high-level data-flow language and execution framework for parallel computation. It is built on top of Hadoop Core.
  • Hive a data warehouse infrastructure which allows sql-like adhoc querying of data (in any format) stored in Hadoop
  • ZooKeeper is a high-performance coordination service for distributed applications.
  • Hama, a Google’s Pregel-like distributed computing framework based on BSP (Bulk Synchronous Parallel) computing techniques for massive scientific computations.
  • Mahout, scalable Machine Learning algorithms using Hadoop

Conclusion: Hope this post helped you in understanding the basic concepts of Big Data and also to setup a Hadoop Single Node Installation to play with. Please do post your thoughts on how Big Data is playing a major role in your organisations.

ArcSight CORR 6.0 – Install and Migration


ArcSight (now HP) Enterprise Security Manager (ESM) is the premiere security event manager that analyzes and correlates every other event in order to support the Security Team or analysts in every aspect of security event monitoring, from compliance and risk management to security intelligence and operations. There have been several versions of ArcSight ESM released over a period in time. Their latest version is ArcSight CORR 6.0. At InfoSecNirvana.com we have got a copy of the latest version and we will be writing a multi-part post on how to Install, Migrate from Older versions to 6.0 and some basic walk around.

In this Part 1 post, we shall cover about the installation of ArcSight CORR (Correlation Optimized Retention and Retrieval), a proprietary data storage and retrieval framework that receives and processes events at high rates, and performs high-speed searches; the latest ArcSight ESM by HP. With the ArcSight CORR, Oracle database is now eliminated.

CORR components:

  • ArcSight Manager
  • CORR Engine
  • ArcSight Console
  • ArcSight Web
  • Management Console
  • Smart Connectors

Requirements:

System: This completely depends on the EPS that you expect to receive. InfoSecNirvana has been working on getting a PoC for this and the below configuration was used:
A VMWare box with 8 cores, 32GB Ram, 256GB SSD HDD, 2TB WD 7200 RPM SATA HDD (Note: for production, there might be/recommend a higher configuration. Check with ArcSight manuals on the same)

OS: Red Hat Enterprise Linux Server release 6.2 x64, installed with xfsprogs-3.1.1-6.el6.x86_64 rpm; this is required to convert some of the ext4 file systems to xfs filesystems. XFS Partition is the most apt format for us to fully utilize the performance enhancements coming with CORR. Typically, I would recommend /opt/ to be formatted with XFS and maximum storage can be allocated to this partition. This is crucial because, the very first step of installation would verify whether the entire /opt/ directory is in XFS. When using VMWare with LVM, we faced some issues during the installation and ArcSight Support could not help us with this. However, when raw devices were mounted as /OPT/ we did not face any issues.

Storage: Please allocate the required storage (calculate based on Number of Devices, Events per second, Average Event Size and Retention period). Remember, CORR is like an ESM with a built in Logger. You can still use a Logger for long term retention if that is what you prefer so that ESM will be lean and mean.

Permissions: The installation has to be done using a Non-Root account. This account can be a service account named”arcsight”. This account should have RWX permissions on the /opt/ directory. Make sure this is satisfied.

Misc: /TMP/ partition should have at least 3GB space. /home/arcsight also should have a minimum of 5GB free space. This is crucial again because, the INSTALL DIR log files are written in these location and if sufficient space is not allocated the installation fails.

The CORR package: Get the CORR installation package and the license from HP ArcSight. This can be obtained from your sales representative with HP/ArcSight.

CORR Installation:
The installation is pretty straightforward and is just a series of clicks. I have given most of the screenshots below just as a reference. Obviously, if you have already installed ArcSight Software, you would not even need this. Once done, you would be able to install the Console to access CORR and play around.



Once the installation is completed, we would want to test the following before we call the install as complete:

  1. Validate the Log Files in the Manager Install Logs and find out if there are any warnings and errors. Generally, this is a best practice to ensure valid installation.
  2. Install the Console and try to connect to ESM, with the default user name and password (mentioned in the install guide). First time when you connect, A certificate import of the Manager happens. If you use a self-signed certificate make sure you note down the parameters used to create cause this will help in future migrations, troubleshooting or recovery.
  3. After connecting to the console, you are ready to go.

Migrations from Existing Installs – Migrating from earlier versions to this CORR instance is tricky, because you are migrating from a DB back end to a NON-DB back end. I will be posting a followup of this post in PART 2 that will detail the migration procedure from 4.X to 5.X.

Stay Tuned to InfoSecNirvana.com for more!!!