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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Tags: CSIRT, csirt-playbook, incident response, infosec, logging, logs, NCSAM, ncsam-2013, playbook, security, SIEM
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.
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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Tags: CSIRT, csirt-playbook, incident response, logging, logs, ncsam-2013, SIEM
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 »
Tags: CSIRT, csirt-playbook, incident response, logging, logs, NCSAM, ncsam-2013, security, SIEM
Your network, servers, and a horde of laptops have been hacked. You might suspect it, or you might think it’s not possible, but it’s happened already. What’s your next move?
The dilemma of the “next move” is that you can only discover an attack either as it’s happening, or after it’s already happened. In most cases, it’s the latter, which justifies the need for a computer security incident response team (CSIRT). Brandon Enright, Matthew Valites, myself, and many other security professionals constitute Cisco’s CSIRT. We’re the team that gets called in to investigate security incidents for Cisco. We help architect monitoring solutions and strategies and enable the rest of our team to discover security incidents as soon as possible. We are responsible for monitoring the network and responding to incidents discovered both internally by our systems or reported to us externally via firstname.lastname@example.org.
Securing and monitoring a giant multinational high-speed network can be quite a challenge. Volume and diversity, not complexity, are our primary enemies when it comes to incident response. We index close to a terabyte of log data per day across Cisco, along with processing billions of NetFlow records, millions of intrusion detection alarms, and millions of host security log records. This doesn’t even include the much larger data store of authentication and authorization data for thousands of people. Naturally, like all large corporations, dedicated attackers, hacking collectives, hacktivists, and typical malware/crimeware affect Cisco. Combine these threats with internally sourced security issues, and we’ve got plenty of work cut out for us.
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Tags: Cisco Security, cisco sio, CSIRT, csirt-playbook, incident response, infosec, logging, logs, playbook, security, SIEM
Following my previous blog post about identity and device aware IT platforms making IT operations easier and more effective, I wanted to delve a little deeper into a specific element of the IT infrastructure: Security Event & Information Management (SIEM) and Threat Defense (TD) systems.
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Tags: event monitoring, ISE, security, SIEM