Splunk recently announced new machine learning capabilities in its Splunk Cloud and Splunk Enterprise 6.5 release. Does everyone have machine learning capabilities now? What exactly is machine learning? See below for key considerations for this technology approach and how indeni’s machine learning differs from Splunk.
3 things IT needs to know about machine learning
- Machine learning algorithms have been around for decades. Most of them, especially those that are mathematically based, are not new. For example Arthur Samuel coined the term “machine learning” in 1959!
- Machine learning works best with large sets of data. You need a substantial amount of information to determine trends, correlations, etc. Take the example of the NVIDIA self-driving car that was shown at CES this year. Only after 3000 miles of driving on highways, back roads and suburban roads was the car able to stop running over traffic cones and avoid parked cars.
- If not constrained, Machine learning will have a very high false positive rate. To continue the analogy from above, say you are monitoring multiple types of automobiles. Comparing the device data of a semi-truck to a Tesla would be interesting, but not actionable. Say one of your rules was to alert if the engine noise exceeded 100 decibels, as you believe this level of noise indicates there is an issue with the engine. A semi-truck would generate an alert every time it turned on, whereas a Tesla would hardly say a peep. Giving your machine learning constraints (eg. compare Tesla data only with other Tesla’s) yields far more accurate results.
Moral of the story, if a vendor pitches you on “machine learning” it’s OK to be optimistic but be cautious. Here are some questions you can ask to see if the machine learning will make your team more productive:
- How does the vendor help the algorithm focus on the important elements? How do they help their technology understand the data to reach the right conclusions?
- How do they avoid a high rate of false positives? For example, if their machine learning algorithms find “an anomaly” what are the chances it’s a true positive?
- How does the vendor make its alerts or findings actionable?
4 ways indeni machine learning differs from Splunk
Now that we are on the same page for machine learning, here are four ways that indeni differs from SIEM and Log Management solutions such as Splunk.
#1 indeni ingests configuration data in addition to statistics and logs of devices.
Collecting greater depths of information on devices and the software running on them allows indeni to identify issues with greater accuracy.
#2 indeni has the largest database of device knowledge.
indeni has a growing repository of known infrastructure issues and resolution steps for the largest Enterprises. This information is gathered from our customers, indeni engineers and third party experts around the globe. How does this help on a daily basis?
- Root cause analysis: Instead of coming up with a hypothesis and then building a query in Splunk so that you can schedule alerts when the same log or event occurs, indeni has the knowledge built into its core alerting system, no scripting or queries required.
- Troubleshooting: When you receive an alert in indeni, in addition to telling you the affected device or error code, indeni provides a human readable description, the implication of not addressing the event and steps to resolution, helping network and security operations teams prioritize focus areas and shorten the mean time to resolution.
#3 indeni connects admins and engineers across the globe
In addition to applying machine learning to the data in your environment, indeni learns from other indeni customers and applies those learnings to your indeni instance. Our users subscribe to a service called “indeni Insight,” which sends data from their environment to our central repository. The data is sanitized and contains general device characteristics and behavior information. For example what model the device is, what software is running on it, which features are enabled, the status of licenses or contracts, running metrics (CPU, memory, etc.), system logs, active users and much more. The result for administrators and engineers? It’s like leveraging the expertise of thousands of your IT operations friends at Fortune 500 companies.
#4 indeni’s algorithmic model is based on the assumption 99.9% of the time devices are working as expected.
Based on our experience as network and security professionals, we know a device malfunctions only 0.1% of the time. In addition, it is widely documented that 70% of network outages occur due to device misconfigurations. These two constraints are built into our machine learning algorithm which allow us to reduce false positives, saving our customers time and money.
At a glance: indeni vs. Splunk
indeni is capable of identifying specific issues, which pertain to specific types of products and even specific software builds, at a level of accuracy and actionability never seen before. With indeni customers can find health and operational issues before they happen in their infrastructure, proactively handle them and have a better life. Interested in trying indeni in your environment? Contact us or engage with one of our registered partners.