Network-based Threat Detection: Looking for Indicators

Now that RSAC is behind us, it’s time to get back to our research agenda. So we pick up Network-based Threat Detection where we left off. In that first post, we made the case that math and context are the keys to detecting attacks from network activity, given that we cannot totally prevent endpoint compromise. Attackers always leave a trail on the network. So we need to collect and analyze network telemetry to determine whether the communication between devices and the content of communications are legitimate, or warrant additional investigation. Modern malware relies heavily on the network to initiate the connection between the device and the controller, download attacks, perform automated beaconing, etc. Fortunately these activities show a deterministic pattern, which enables you to pinpoint malicious activity and identify compromised systems. Attackers bet they will be able to obscure their communications within the tens of billions of legitimate packets traversing enterprise networks on any given day, and on defenders’ general lack of sophistication preventing them from identifying the giveaway patterns. But if you can identify the patterns, you have an opportunity to detect attacks. Command and Control Command and Control (C&C) traffic is communication between compromised devices and botnet controllers. Once the device executes malware (by whatever means) and the dropper is installed, the device searches for its controller to receive further instructions. There are two main ways to identify C&C activity: traffic destination and communications patterns between. The industry has been using IP reputation for years to identify malicious destinations on the Internet. Security researchers evaluate each IP address and determine whether it is ‘good’ or ‘bad’ based on activity they observe across a massive network of sensors. IP reputation turns out to be a pretty good indicator that an address has been used for malicious activity at some point. Traffic to known-bad destinations is definitely worth checking out, and perhaps even blocking. But malicious IP addresses (and even domains) are not active for long, as attackers cycle through addresses and domains frequently. Attackers also use legitimate sites as C&C nodes, which can leave innocent (but compromised) sites with a bad reputation. So the downside to blocking traffic to sites with bad reputation is the risk of irritating users who want to use the legitimate site. Our research shows increasing comfort with blocking sites because the great majority of addresses with bad reputations have legitimately earned it. Keep in mind that IP reputation is not sufficient to identify all the C&C traffic on your network – many malicious sites don’t show up on IP reputation lists. So next look for other indications of malicious activity on the network, which depends on how compromised devices find their controllers. With the increasing use of domain generating algorithms (DGA), malware doesn’t need to be hard-coded with specific domains or IP addresses – instead it cycles through a set of domains according to its DGA, searching for a dynamically addressed C&C controller; the addresses cycle daily. This provides tremendous flexibility for attackers to ensure newly-compromised devices can establish contact, despite frequent domain takedowns and C&C interruptions. But these algorithms look for controllers in a predictable pattern, making frequent DNS calls in specific patterns. So DNS traffic analysis has become critical for identification of C&C traffic, along with monitoring packet streams. Outliers Identifying C&C traffic before the compromised device becomes a full-fledged member of the botnet is optimal. But if you miss, once the device is part of the botnet you can look for indications that it is being used as part of an attack chain. You do this by looking for outliers: devices acting atypically. Does this sound familiar? It should – anomaly detection has been used to find attackers for over a decade, typically using Netflow. You profile normal traffic patterns for users on your network (source/destination/protocol), and then look for situations where traffic varies outside your baseline and exceeds tolerances. Network-based anomaly detection was reasonably effective, but as adversaries got more sophisticated detection needed to dig more deeply into traffic. Deep packet inspection and better analytics enabled detection offerings to apply context to traffic. Attack traffic tends to occur in a few cycles: Command and Control: As described above, devices communicate with botnet controllers to join the botnet. Reconnaissance: After compromising the device and gaining access via the botnet, attackers communicate with internal devices to map the network and determine the most efficient path to their target. Lateral Movement: Once the best path to the target is identified, attackers systematically move through your network to approach their intended target, by compromising additional devices. Exfiltration: Once the target device is compromised, the attacker needs to move the data from the target device, outside the network. This can be done using tunnels, staging servers, and other means to obfuscate activity. Each of these cycles includes patterns you can look for to identify potential attacks. But this still isn’t a smoking gun – at some point you will need to apply additional context to understand intent. Analyzing content in the communication stream is the next step in identifying attacks. Content One way to glean more context for network traffic is to understand what is being moved. With deep packet inspection and session reassembly, you can perform file-based analysis on content as well. Then you can compare against baselines to look for anomalies in the movement of content within your network as well. File size: For example, if a user moved 2gb of traffic over a 24 hour period, when they normally move no more than 100mb, that should trigger an alert. Perhaps it’s nothing, but it should be investigated. Time of day: Similarly, if a user doesn’t normally work in the middle of the night, but does so two days in a row by themselves, that could indicate malicious activity. Of course it might be just a big project, but it bears investigation. Simple DLP: You can fingerprint files to look for sensitive content, or regular expressions which match account numbers or other protected data. Of course that isn’t full DLP-style classification and analysis. But it could flag something malicious without the

Read Post

Totally Transparent Research is the embodiment of how we work at Securosis. It’s our core operating philosophy, our research policy, and a specific process. We initially developed it to help maintain objectivity while producing licensed research, but its benefits extend to all aspects of our business.

Going beyond Open Source Research, and a far cry from the traditional syndicated research model, we think it’s the best way to produce independent, objective, quality research.

Here’s how it works:

  • Content is developed ‘live’ on the blog. Primary research is generally released in pieces, as a series of posts, so we can digest and integrate feedback, making the end results much stronger than traditional “ivory tower” research.
  • Comments are enabled for posts. All comments are kept except for spam, personal insults of a clearly inflammatory nature, and completely off-topic content that distracts from the discussion. We welcome comments critical of the work, even if somewhat insulting to the authors. Really.
  • Anyone can comment, and no registration is required. Vendors or consultants with a relevant product or offering must properly identify themselves. While their comments won’t be deleted, the writer/moderator will “call out”, identify, and possibly ridicule vendors who fail to do so.
  • Vendors considering licensing the content are welcome to provide feedback, but it must be posted in the comments - just like everyone else. There is no back channel influence on the research findings or posts.
    Analysts must reply to comments and defend the research position, or agree to modify the content.
  • At the end of the post series, the analyst compiles the posts into a paper, presentation, or other delivery vehicle. Public comments/input factors into the research, where appropriate.
  • If the research is distributed as a paper, significant commenters/contributors are acknowledged in the opening of the report. If they did not post their real names, handles used for comments are listed. Commenters do not retain any rights to the report, but their contributions will be recognized.
  • All primary research will be released under a Creative Commons license. The current license is Non-Commercial, Attribution. The analyst, at their discretion, may add a Derivative Works or Share Alike condition.
  • Securosis primary research does not discuss specific vendors or specific products/offerings, unless used to provide context, contrast or to make a point (which is very very rare).
    Although quotes from published primary research (and published primary research only) may be used in press releases, said quotes may never mention a specific vendor, even if the vendor is mentioned in the source report. Securosis must approve any quote to appear in any vendor marketing collateral.
  • Final primary research will be posted on the blog with open comments.
  • Research will be updated periodically to reflect market realities, based on the discretion of the primary analyst. Updated research will be dated and given a version number.
    For research that cannot be developed using this model, such as complex principles or models that are unsuited for a series of blog posts, the content will be chunked up and posted at or before release of the paper to solicit public feedback, and provide an open venue for comments and criticisms.
  • In rare cases Securosis may write papers outside of the primary research agenda, but only if the end result can be non-biased and valuable to the user community to supplement industry-wide efforts or advances. A “Radically Transparent Research” process will be followed in developing these papers, where absolutely all materials are public at all stages of development, including communications (email, call notes).
    Only the free primary research released on our site can be licensed. We will not accept licensing fees on research we charge users to access.
  • All licensed research will be clearly labeled with the licensees. No licensed research will be released without indicating the sources of licensing fees. Again, there will be no back channel influence. We’re open and transparent about our revenue sources.

In essence, we develop all of our research out in the open, and not only seek public comments, but keep those comments indefinitely as a record of the research creation process. If you believe we are biased or not doing our homework, you can call us out on it and it will be there in the record. Our philosophy involves cracking open the research process, and using our readers to eliminate bias and enhance the quality of the work.

On the back end, here’s how we handle this approach with licensees:

  • Licensees may propose paper topics. The topic may be accepted if it is consistent with the Securosis research agenda and goals, but only if it can be covered without bias and will be valuable to the end user community.
  • Analysts produce research according to their own research agendas, and may offer licensing under the same objectivity requirements.
  • The potential licensee will be provided an outline of our research positions and the potential research product so they can determine if it is likely to meet their objectives.
  • Once the licensee agrees, development of the primary research content begins, following the Totally Transparent Research process as outlined above. At this point, there is no money exchanged.
  • Upon completion of the paper, the licensee will receive a release candidate to determine whether the final result still meets their needs.
  • If the content does not meet their needs, the licensee is not required to pay, and the research will be released without licensing or with alternate licensees.
  • Licensees may host and reuse the content for the length of the license (typically one year). This includes placing the content behind a registration process, posting on white paper networks, or translation into other languages. The research will always be hosted at Securosis for free without registration.

Here is the language we currently place in our research project agreements:

Content will be created independently of LICENSEE with no obligations for payment. Once content is complete, LICENSEE will have a 3 day review period to determine if the content meets corporate objectives. If the content is unsuitable, LICENSEE will not be obligated for any payment and Securosis is free to distribute the whitepaper without branding or with alternate licensees, and will not complete any associated webcasts for the declining LICENSEE. Content licensing, webcasts and payment are contingent on the content being acceptable to LICENSEE. This maintains objectivity while limiting the risk to LICENSEE. Securosis maintains all rights to the content and to include Securosis branding in addition to any licensee branding.

Even this process itself is open to criticism. If you have questions or comments, you can email us or comment on the blog.