AlienVault OSSIM vs Elasticsearch

• Asset discovery. Once installed in a centric, network-accessible server, OSSIM can poll all your endpoints with common protocols (SSH, SNMP, WMI) to detect and discover site-wide assets to monitor. You only need to group them by your own criteria once added to the product.
• SIEM Event Correlation. You can define quite complex correlation rules to detect possible suspicious or malicious actions or attempts in your network, in order to categorize them as real threats or as false positives, thus streamlining your risk assessment and management.
• Ease of installation. The entire AlienVault OSSIM is self-contained in an ISO file, which can be burned into a DVD or just mounted in your server of choice (physical or virtual) for deployment. The installation process is automated and quote verbosed, with options for static IP, email messaging and others.
• Ease of access. Being AlienVault OSSIM a self-contained appliance, it can be accessed via web by any device that supports a web browser, being that desktops, workstation, mobile devices, etc. The OSSIM dashboard and other features are automatically rearranged to adapt to the particular device being in use.

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• Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range.
• Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system
• Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.

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• The reports are clunky and a bit tedious to parse through.
• Sometimes there's so much noise it's hard to tell what a true positive is. There are lots of false ones that trigger alerts but are normal behavior in many environments.

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• Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
• The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
• This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.

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• The only investment here is setting it up and I think seeing it's performance it's a fantastic tool and has a great positive ROI!

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• Faster searches on our application have resulted in better usability and increased application use
• Analytics dashboard has given our managers a better understanding of day-to-day activities
• Being a backup data store, we need not touch SQL database while doing data dumps for local data science projects

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