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Securing Hadoop Operational Security Issues

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Securing Hadoop Operational Security Issues

Par Security Bloggers Network
Le [2016-02-11] à 04:43:33



Présentation : Beyond the architectural security issues endemic to Hadoop and NoSQL platforms we discussed in the last post, there are common security processes and supporting tools IT teams expect from other data management platforms. That includes turning the dials on configuration management, vulnerability assessment, and maintaining patch revisions across a complex assembly of supporting modules. For most IT systems, the day to day processes IT managers follow to ensure application platforms are properly configured have evolved over many years, both in terms of core platform capabilities, as well as community contributions and 3rd party commercial support to fill in the gaps. Best practices, checklists and validation tools which verify things like admin rights are sufficiently tuned, and that nodes are patched to remediate vulnerabilities. Hadoop security has come a long way in just a few years, but it still lacks the maturity in day to day operational security offerings, and it is here that we find most firms continue to struggle. The following is an overview of the most common threats to data management systems, where the operational controls provide preventative security measures, closing the door on most common attacks. As with the previous section, here we will discuss the challenges, then map those challenges to one or more security options to mitigate the threat. Authentication and authorization Identity and authentication are central concepts to any security effort without them we cannot judge who should get access to data. And the one area we have seen the biggest change in with identity and access management. Thanks in large part to the providers of enterprise distributions of Hadoop for doing a lot of the integration and setup work. We have moved beyond simple in database authentication and crude platform identity management to far more seamless LDAP, Active Directory, Kerberos and X.509 based authentication options. Leveraging those capabilities, we can use established roles for authorization mapping, and in some cases, extend to fine-grained authorization services with Apache Sentry, or custom auth mapping from within the calling application or from within the database. Administrative data access Most organizations have platform administrators and NoSQL database administrators, both with access to the files within the cluster. To provide separation of duties, or more more succinctly stated, ensure administrators cannot view content, some facility is needed to both segregate administrative roles and to keep unwanted access to a minimum. Direct access to files or data is commonly addressed through a combination of role based authorization, access control lists, file permissions, and segregation of admin roles i.e. different admin accounts with different roles and credentials . This provides basic protection but still does not protect content when archived or snapshotted. For stronger security a combination of data encryption and key management services, where unique keys for each application file cluster, should be employed. This ensures different tenants apps in the cluster from viewing others data. Configuration and Patch Management With a clusters of servers, where you may have hundreds of nodes, it is common to have different configurations and patch levels running at the same time. As new nodes are added we see configuration skew. Keeping track of revisions is difficult. Existing configuration management tools work for underlying platforms, and HDFS Federation will help with cluster management, but these features still leave a lot to be desired. Issuing encryption keys, avoiding ad-hoc configuration changes, ensuring file permissions are set correctly, ensuring TLS is correctly configured and dozens of other potential configuration issues. There is no counterpart for the configuration management tools available on relational platforms, and even commercial Hadoop distributions offer scant advice on recommended configurations and pre-deployment checklists. What we are seeing however, with databased deployed atop virtualized servers and cloud deployments, are home-grown pre-deployment scripts to ensure configuration scripts, patches and open source code revisions are consistent. Akin to the concept of a gold master all aspects of setup and validation are performed automatically prior to nodes being added to the pool. Software Bundles The application stack and the Hadoop stack are assembled from many different components. Underlying platforms and file systems are also variable, with their own configuration settings, ownership rights and patch levels. We see increasing number of organizations use source code control systems to manage open source version management and application stack management. Container technologies also aid developers in bundling and consistent application deployments. Authentication of applications and nodes If an attacker can add a new node under their control to the cluster, it s possible for them to exfiltrate data from the cluster. To authenticate nodes as opposed to users before joining a cluster, most firms we spoke with either employed X.509 certificates or Kerberos. Both off these features can authenticate users as well, but we draw this distinction to underscore the potential threat vector of rogue applications or nodes being added to the cluster. The deployment of these services come with some risks as well for example, if a Kerberos keytab files can be accessed or duplicated perhaps using credentials extracted from virtual image files or snapshots - node identity can be forged. All these certificate based identity options come with some pain of setup and deployment, We know it is a pain to set up, but properly deployed, offer strong authentication and a genuine advancement to security. Audit and Logging If you suspect someone has breached your cluster, can you detect it or trace back the events that caused it You need a record of activity, which is usually provided by event logging. There are a variety of add-on logging capabilities, both open source and commercial. Scribe and LogStash are open source tools that integrate into most big data environments, as do a number of commercial products. You can leverage the existing cluster to store logs, set up an independent cluster, or even leverage other dedicated platforms like a SIEM or Splunk. That said, some of the logging options we have seen do not contain enough information to provide an auditor with enough information to determine exactly what actions occurred. You will need to verify that the logs are capturing both the correct event types, as well as the user actions it s not enough to have a user Id and an IP address, but you need to know what queries were issued. Monitoring, filtering, and blocking There are no built-in monitoring tools to look for misuse or block malicious queries. In fact there is no consensus on what a malicious big data query looks like aside from crappy MapReduce scripts written by bad programmers. We are just now seeing the first viable releases of activity monitoring tools on Hadoop. No longer the after-market speed regulators they once were, these tools commonly embedded into a service like Hive or Spark to capture queries. As the use of SQL queries has blossomed in the last couple years, we can leverage database activity monitoring technologies to flag misuse or even block. At present these tools are still very new, but this technology approach has long proven effective on relational platforms and should become better with time. API security The APIs for big data clusters need to be protected from code and command injection, buffer overflow attacks, and every other web services attack. Most of the time this responsibility falls upon the application s that use the cluster, but not always. Common security controls include integration with directory services, mapping OAuth tokens to API services, filtering requests, input validation, managing policies across nodes, and so on. Some people have leveraged API gateways, and white-listed the set of allowable application requests. Again, there are a handful of off-the-shelf solutions to help address API security issues, but most are based on a gateway that funnels users and all requests through a single interface. Additionally DevOps approaches to application stack patching and pre-deployment validation are proving very effective at addressing application and cluster security issues. There are many, many API security issues, but a full examination is beyond the scope of this research paper Threat-response models For each of the threats we have identified above, there are one or more security countermeasures to mitigate that threat. The following diagram shows some options you have at your disposal Threat response model In our next post we will discuss the strategic security models and how some of these security technologies are deployed. It s here you will see how some deployments are aiming for simplicity and ease of management instead of attempting to achieve the best security that they can. For example, some firms we interviewed used Kerberos to uniquely identify nodes on the cluster and leverage Kerberos certificates to prove identity. Other firms we spoke with issued TLS certificates to each node prior to adding it to the cluster this serves as a form of bi-directional identification between nodes and for session encryption, but not really node authentication. The tradeoff being Kerberos offers much greater security in terms of identity and enforcement but typically comes with greater cost in set-up and management costs. - Adrian Lane 0 Comments Subscribe to our daily email digest

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