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Using a “Playbook” Model to Organize Your Information Security Monitoring Strategy

CSIRT, I have a project for you. We have a big network and we’re definitely getting hacked constantly. Your group needs to develop and implement security monitoring to get our malware and hacking problem under control.

 

If you’ve been a security engineer for more than a few years, no doubt you’ve received a directive similar to this. If you’re anything like me, your mind probably races a mile a minute thinking of all of the cool detection techniques you’re going to develop and all of the awesome things you’re going to find.

I know, I’ll take the set of all hosts in our web proxy logs doing periodic POSTs and intersect that with…

STOP!

 

You shouldn’t leap before you look into a project like this. Read More »

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To SIEM or Not to SIEM? Part II

The Great Correlate Debate

SIEMs have been pitched in the past as “correlation engines” and their special algorithms can take in volumes of logs and filter everything down to just the good stuff. In its most basic form, correlation is a mathematical, statistical, or logical relationship between a set of different events. Correlation is incredibly important, and is a very powerful method for confirming details of a security incident. Correlation helps shake out circumstantial evidence, which is completely fair to use in the incident response game. Noticing one alarm from one host can certainly be compelling evidence, but in many cases it’s not sufficient. Let’s say my web proxy logs indicate a host on the network was a possible victim of a drive-by download attack. The SIEM could notify the analysts team that this issue occurred, but what do we really know at this point? That some host may have downloaded a complete file from a bad host -- that’s it. We don’t know if it has been unpacked, executed, etc. and have no idea if the threat is still relevant. If the antivirus deleted or otherwise quarantined the file, do we still have anything to worry about? If the proxy blocked the file from downloading, what does that mean for this incident?

This is the problem that correlation can solve. If after the malware file downloaded we see port scanning behavior, large outbound netflow to unusual servers, repeated connections to PHP scripts hosted in sketchy places, or other suspicious activity from the same host, we can create an incident for the host based on our additional details. The order is important as well. Since most attacks follow the same pattern (bait, redirect, exploit, additional malware delivery, check-in), we tie these steps together with security alarms and timestamps. If we see the events happening in the proper order we can be assured an incident has occurred.

 

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To SIEM or Not to SIEM? Part I

Security information and event management systems (SIEM, or sometimes SEIM) are intended to be the glue between an organization’s various security tools. Security and other event log sources export their alarms to a remote collection system like a SIEM, or display them locally for direct access and processing. It’s up to the SIEM to collect, sort, process, prioritize, store, and report the alarms to the analyst. It’s this last piece that is the key to an effective SIEM deployment, and of course the most challenging part. In the intro to this blog series I mentioned how we intend to describe our development of a new incident response playbook. A big first step in modernizing our playbook was a technology overhaul, from an outdated and inflexible technology to a modern and highly efficient one. In this two-part post, I’ll describe the pros and cons of running a SIEM, and most importantly provide details on why we believe a log management system is the superior choice.

Deploying a SIEM is a project. You can’t just rack a new box of packet-eating hardware and expect it to work. It’s important to understand and develop all the proper deployment planning steps. Things like scope, business requirements, and engineering specifications are all factors in determining the success of the SIEM project. Event and alarm volume in terms of disk usage, and retention requirements must be understood. There’s also the issue of how to reliably retrieve remote logs from a diverse group of networked devices without compatibility issues. You must be able to answer questions like: Read More »

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Getting a Handle on Your Data

When your incident response team gets access to a new log data source, chances are that the events may not only contain an entirely different type of data, but may also be formatted differently than any log data source you already have. Having a data collection and organization standard will ease management and analysis of the data later on. Event attributes must be normalized to a standard format so events from disparate sources have meaning when viewed homogeneously. In addition to normalization, log events must be parsed into fields and labeled in a consistent way across data sources. Ensuring that log data is organized properly is a minimum requirement for efficient log analysis. Without digestible and flexible components, it’s extremely difficult to comprehend a log message. If you have ever paged through screen after screen of log data with no filter, you know what I’m talking about.

Normalization

Data normalization is the process of transforming a log event into its canonical form, that is, the accepted standard representation of the data required by the organization consuming the data. If the same data can be represented in multiple formats, each possible iteration of the data can be considered a member of an equivalence class. To allow proper sorting, searching, and correlation, all data in the equivalence class must be formatted identically.

As an example, let’s consider timestamps. The C function strftime and its approximately 40 format specifiers give an indication of the potential number of ways a date and time can be represented. The lack of an internationally recognized standard timestamp format, combined with the fact that most programming libraries have adopted strftime’s conversion specifications, means that application developers are free to define timestamps as they see fit. Consuming data that includes timestamps requires recognizing the different formats and normalizing them to an organization’s adopted standard format. Other data contained in logs that may require normalization includes MAC addresses, phone numbers, alarm types, IP addresses, and DNS names. These are examples of equivalence classes, where the same data may be represented by different applications in different formats. In the case of an IP address or a DNS name, the CSIRT may find it beneficial not to normalize the data in-place, but rather to create an additional field, the labels of which are standardized across all data sources where possible.

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Making Boring Logs Interesting

In the last week alone, two investigations I have been involved with have come to a standstill due to the lack of attribution logging data. One investigation was halted due to the lack of user activity logging within an application, the other from a lack of network-based activity logs. Convincing the asset owners of the need for logging after-the-fact was easy. But ideally, this type of data would be collected before it’s needed for an investigation. Understanding what data is critical to log, engaging with the asset owners to ensure logs contain meaningful information, and preparing log data for consumption by a security monitoring organization are ultimately responsibilities of the security monitoring organization itself. Perhaps in a utopian world, asset owners will engage an InfoSec team proactively and say, “I have a new host/app. To where should I send my log data which contains attributable information for user behavior which will be useful to you for security monitoring?” In lieu of that idealism, what follows is a primer on logs as they relate to attribution in the context of security event monitoring. Read More »

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