Centralized Log Management (CLM)

Event Log Convergence = Business Intelligence

Problem

I have come across many prospects over the last 6 years that are only trying to acquire a SIEM solution to satisfy a compliance requirement, or what we call in the industry, “check-box purchasing” – they have a minimum set of requirements, specific to only one business unit or compliance mandate that is completely siloed from the rest of the organization.

Here is how this conversation usually goes:

Client: “we would like a SIEM tool that will help us monitor our 200+ Windows Servers for PCI-DSS compliance”

Me: “What other event sources are you going to be monitoring with the solution?”

Client: (Stunned look) “we only need to monitor our servers.”

Me: “PCI-DSS requirement 10 states you have to monitor the logs from all of your security devices and servers that are deemed critical assets.”

Client: “our department is only responsible for the servers we listed.”

Me: “To get value out of a SIEM solution and monitor all 12 PCI requirements you need audit logs from all of your devices and contextual information regarding your network, asset and vulnerability data – and that will just get you started.”

Client: “Perhaps we need to increase the scope – we’ll get back to you.”

While the Centralized Log Management (CLM) and Security Information and Event Management (SIEM) vendors will be lined up around the block to influence the sale, the vendor you choose should be a trusted advisor. They will be interested in providing you the most value from your investment and assist you in designing a solution to satisfy many business problems that goes beyond a traditional security-centric SIEM. This is why you will need to identify key device types and the value that can be derived by cross correlating the log data with business context to align monitoring with your governance, security and compliance initiatives.

The SIEM Value Derived from Heterogeneous Device Logging

While each of the event sources you collect events from will provide distinct reporting and alerting value, combining many different “types” of event sources will derive immediate intelligence about the business and help analysts establish baselines of threat activity. One of the other benefits is that incidents can be prioritized by business value, threat classification and the additional context can help reduce the plethora of false-positives or false-negatives that plagues every CLM solution.

Additionally, the multitude of the various technologies have their own management and reporting solutions that become “silos of information” that only the black-belts responsible for each of the device types are able to decipher. This makes security intelligence and investigations near impossible when an analyst has to request log data from the owners or log in to many different systems to find the evidence to support their cause. Essentially, they would have to piece together the clues and manually normalize the data using a technique called “Spreadsheet Kung Fu”, which would be fraught with assumptions and inference.

Below is a list of different device types and the value that can be derived from each when correlated together. The list isn’t exhaustive and I’m not suggesting you need everyone mentioned to successfully deploy a SIEM, but the more data feeds you can correlate, the more intelligence you will have available in the future to expand and grow with your business (click “more…” for complete article):

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Determining Peak EPS Calculations in Logging

Much of the challenge in sizing and planning Centralized Log Management (CLM) and Security Information and Event Management (SIEM) solutions is determining an adequate amount of storage for storing logs for real-time analysis and archiving the log data to meet long-term retention requirements. The biggest challenge most customers face is determining the required metrics needed in sizing a solution. My post “Basic Log Storage Calculations” http://www.buzzcircuit.com/?p=208 can assist in the sizing and variables needed and my post “Guessing Game – Planning & Sizing SIEM Based on EPS” http://www.buzzcircuit.com/?p=231 can help with guessing the EPS averages for each device types. Finally, I have a couple of cool calculators at http://www.buzzcircuit.com/?p=408 and http://www.buzzcircuit.com/?p=378 that can actually assist with the final calculations.

At this point you have probably guessed that log storage calculations and storage planning is somewhat of an art, rather than a science – there’s a lot of guesswork involved, especially if you don’t have access to the systems or network devices hosting the logs. While I have done a good job (I think) in helping you dispel some of the myths and “guesstimating” an overall log capacity in previous posts, one area that is often overlooked in planning log management or SIEM is the concept of Normal EPS (NE) vs Peak EPS (PE) and ensuring your daily calculation provide a necessary contingency for consistent peaks in your event logging throughout the day.

Normal vs Peak Logging

There are two basic calculations when combining normal + peak EPS, which by no means is a hard rule. The idea is that their is the NUMBER_OF_PEAKS multiplied by the DURATION_OF_EACH_PEAK, which is then multiplied by the DEVIATION_FACTOR. To describe each of these points:
  • NUMBER_OF_PEAKS: calculating Peak EPS (PE) is required to factor in Normal EPS with Peaks (expressed as NE+PE) to ensure their is sufficient licensing and storage to accommodate periods of deviation from the normal EPS throughout the day. The default in the calculator below assumes there will be at least 3 peaks a day (morning logins, lunchtime web surfing, evening logoffs/backups). This value will vary based on network throttling, congestion, attacks, etc.
  • DURATION_OF_EACH_PEAK: This setting works in conjunction with the previous PE setting and assumes that each peak lasts for approximately 1 hour (3600 seconds) – this may vary given many factors such as how congested the network is, how busy the logging device is or other scenarios such as DDoS attacks.
  • DEVIATION_FACTOR: is generally 2-5x the average EPS for that period. While in reality the EPS spikes almost 20x the average EPS for only seconds, we are building in contingency for attacks such as perimeter devices under DDoS or excessive IT Operational errors that go unnoticed for hours.  NOTE: again, this is an art, not a science and we’ll sound like we know more than our competitors if we think to include contingency into our calculations!

Hope you enjoy!

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NetCerebral’s Device EPS Calculator

Hi folks, this post is another form I created using the Calculated Fields Form plugin for WordPress. Basically, this simple form calculates the number of devices input in the form fields and multiplies the number of devices by the designated Events Per Second (EPS) average for each device type. It then provides a live calculation of total number of devices, total average EPS and total average Events Per Day (EPD).

This handy calculation can then be used on my other calculator NetCerebral’s Simple Log Storage Calculator as the average EPS, need as the primary input to calculate amount of storage and IOPs required for the EPD and retention periods defined.

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NetCerebral’s Simple Log Storage Calculator

The following form assumes you have done the preliminary math of determining your number of devices and the total anticipated Events Per Second (EPS) you will be collecting from all of your logging devices. The calculator uses EPS to determine the Events Per Day (EPD), amount of raw and normalized log data you will generate daily and then use retention and compression values you set to determine the required amount of storage as well as IOPs required.

Stay tuned and I will be building another calculator that will allow you to specify number of devices by device type, which will give you your estimated EPS (needed as a starting point for this calculator).

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