Detection: Humans in the Loop a Must

Detecting incidents is probably the most difficult network security task to perform well and consistently. Did you know that less than one out of five security incidents are detected by the organization being affected? Most organizations only find out they’ve experienced an information security incident when law enforcement comes knocking on their door, if they find out about it at all that is. And that can be very bad for business in the present environment. Customers are increasingly demanding stronger information security measures from their service providers and partners.

In order to have the best chance of detecting network security incidents, you need to record and monitor system activities. However, there is no easier way to shut down the interest of a network security or IT administrator than to say the word “monitoring”. You can just mention the word and their faces fall as if a rancid odor had suddenly entered the room! And I can’t say that I blame them. Most organizations still do not recognize the true necessity of monitoring, and so do not provide proper budgeting and staffing for the function. As a result, already fully tasked (and often times inadequately prepared) IT or security personnel are tasked with the job. This not only leads to resentment, but also virtually guarantees that the job will not be performed effectively.

But all is not gloom and doom. Many companies are reacting to the current business environment and are devoting more resources to protecting their private information. In addition, the security industry is constantly developing new tools that help streamline and remove much of the drudge work from the monitoring and detection tasks. And I surely recommend that businesses employ these tools to their full effect. Use log aggregation tools, parsers, artificial intelligence and whatever else is made available for these jobs.

However, it behooves us not to rely on these new magic bullets too much. As can be easily demonstrated from the history of security in general, there has never been a defense strategy that cannot be overcome by human cleverness and persistence. This continues to be demonstrably true in the world of information security.

My advice is to use the new tools to their maximum effectiveness, but to use them wisely. Only spend enough on the technology to accomplish the jobs at hand; don’t waste your money on redundant tools and capabilities. Instead, spend those savings on information security personnel and training. It will pay you well in the long run.

Revisiting Nuance Detection

The core of nuance detection is to extend alerting capabilities into finding situations that specifically should not exist, and if they happen, would indicate a significant security failure. A simple, elegant example would be a motion sensor on a safe in your home, combined with something like your home alarm system.
 
A significant failure state would be for the motion sensor inside the safe to trigger while the home alarm system is set in away mode. When the alarm is in away mode, there should be no condition that triggers motion inside the safe. If motion is detected, anytime, you might choose to alert in a minor way. But, if the alarm is set to away mode, you might signal all kinds of calamity and flashing lights, bells and whistles, for example.
 
This same approach can apply to your network environment, applications or data systems. Define what a significant failure state looks like, and then create detection and alerting mechanisms, even if conditional, for the indicators of that state. It can be easy. 
 
I remember thinking more deeply about this for the first time when I saw Marcus Ranum give his network burglar alarm speech at Defcon, what seems like a 1000 years ago now. That moment changed my life forever. Since then, I have always wanted to work on small detections. The most nuanced of fail states. The deepest signs of compromise. HoneyPoint™ came from that line of thinking, albeit, many years later. (Thanks, Marcus, you are amazing! BTW.) 🙂
 
I’ve written about approaches to it in the past, too. Things like detecting web shells, detection in depth techniques and such. I even made some nice maturity and deployment models.
 
This month, I will be revisiting nuance detection more deeply. Creating some more content around it, and speaking about it more openly. I’ll also cover how we have extended HoneyPoint with the Handler portion of HoneyPoint Agent. in order to fully support event management and data handling into your security alerting systems from basic scripts and simple tools you can create yourself. 
 
Stay tuned, and in the meantime, drop me a line on Twitter (@lbhuston) and let me know more about nuance detections you can think of or have implemented. I’d love to hear more about it. 

Network Segmentation versus Network Enclaving

As we have discussed in earlier blogs, network segmentation is the practice of splitting computer networks into subnets using combinations of firewalls, VLANs, access controls and policies & procedures. We have seen that the primary reason for segmenting networks is to prevent a simple perimeter breach from exposing the totality of an organization’s information assets. So what is the difference between network segmentation and network enclaving?

One of the differences is just the degree of segmentation you impose upon the network. Enclaves are more thoroughly segmented from the general network environment than usual. In fact, enclaving is sometimes just described as “enhanced network segmentation.”

Another difference between segmentation and enclaving is the primary threat enclaving strives to thwart: the internal threat. Although the preponderance of cyber-attacks come from external threat sources such as hackers, cyber-criminals and nation states, many of the most devastating breaches originate from internal sources such as employees and trusted service providers. These internal information security breaches may be either purposeful attacks or may simply be caused by employee error. Either way, they are just as devastating to an organization’s reputation and business share.

A rarely considered difference between enclaving and network segmentation is physical security. When fully controlling access to information assets based on the principle of need to know, it is not enough to just control logical access. It is necessary to restrict physical access to work areas and computer devices as well. These areas should be locked, and access by authorized personnel should be recorded and monitored. Visitors and service providers should be pre-approved and escorted when in protected areas.

An obvious problem with enclaving is that it is more difficult to implement and maintain than the usual information security measures. It requires more planning, more devices and more employee hours. So why should businesses trying to control expenditures put their resources into enclaving?

As an information security professional I would say that it should be done because it is the best way we know to protect information assets. But for many business concerns, the greatest benefit of true enclaving is in securing protected and regulated information such as payment card information, patient health records and personal financial information. If you employ enclaving to protect such assets, you are showing clients and regulators alike that your business is serious about securing the information in its charge. And in today’s business climate, that can be a very important differentiator indeed!

Network Knowledge and Segmentation

If you look at most cutting-edge network information security guidance, job #1 can be paraphrased as “Know Thy Network.” It is firmly recommended (and in much regulatory guidance demanded) that organizations keep up-to-date inventories of hardware and software assets present on their computer networks. In fact, the most current recommendation is that organizations utilize software suites that not only keep track of inventories, but monitor all critical network entities with the aim of detecting any hardware or software applications that should not be there.

Another part of network knowledge is mapping data flows and trust relationships on networks, and mapping which business entities use which IT resources and information. For this knowledge, I like to go to my favorite risk management tool: the Business Impact Analysis (BIA). In this process, input comes from personnel across the enterprise detailing what they do, how they do it, what resources they need, what input they need, what output they produce and more (see MSI blog archives for more information about BIA and what it can do for your information security program).

About now, you are probably asking what all this has to do with network segmentation. The answer is that you simply must know where all your network assets are, who needs access to them and how they move before you can segment the network intelligently and effectively. It can all be summed up with one phrase: Need to Know. Need to know is the very basis of access control, and access control is what network segmentation is all about. You do not want anyone on your network to “see” information assets that they do not need to see in order to properly perform their business functions. And by the same token, you do not want network personnel to be cut off from information assets that they do need to perform their jobs. These are the reasons network knowledge and network segmentation go hand-in-hand.

Proper network knowledge becomes even more important when you take the next step in network segmentation: enclaving. I will discuss segmentation versus enclaving in my next blog later this month.

Why Segment Your Network?

Network segmentation is the practice of splitting your computer network into subnetworks or network segments (also known as zoning). This is typically done using combinations of firewalls, VLANs, access controls and policies & procedures. Implementing network segmentation requires planning and effort, and it can entail some teething problems along the way as well. So why should it be done?

The number one reason is to protect the security of your network resources and information. When people first started to defend their homes and enterprises from attack, they built perimeter walls and made sure everything important was inside of those walls. They figured doing this would keep their enemies outside where they couldn’t possibly do any damage. This was a great idea, but unfortunately it had problems in the real world.

People found that the enemy only had to make one small hole in their perimeter defenses to be able to get at all of their valuables. They also realized that their perimeter defense didn’t stop evil insiders from wreaking havoc on their valuables. To deal with these problems, people started to add additional layers of protection inside of their outer walls. They walled off enclaves inside the outer defenses and added locks and guards to their individual buildings to thwart attacks.

This same situation exists now in the world of network protection. As network security assessors and advisors, we see that most networks we deal with are still “flat;” they are not really segmented and access controls alone are left to prevent successful attacks from occurring. But in the real world, hacking into a computer network is all about gaining a tiny foothold on the network, then leveraging that access to navigate around the network. The harder it is for these attackers to see the resources they want and navigate to them, the safer those resources are. In addition, the more protections that hackers need to circumvent during their attacks, the more likely they are to be detected. It should also be noted that network segmentation works just as well against the internal threat; it is just as difficult for an employee to gain access to a forbidden network segment as it is for an Internet-based attacker.

Increased security is not the only advantage of network segmentation. Instead of making network congestions worse, well implemented segmentation can actually reduce network congestion. This is because there are fewer hosts, thus less local traffic per segment. In addition, segmentation can help you contain network problems by limiting the effects of local failures that occur on other parts of the network.

The business reasons for implementing network segmentation are becoming more apparent every day. Increasingly, customers are demanding better information security from the businesses they employ. If the customer has a choice between two very similar companies, they will almost assuredly pick the company with better security. Simply being able to say to your customers that your network is fully segmented and controlled can improve your chances of success radically.

Segmenting With MSI MachineTruth

Many organizations struggle to implement network segmentation and secure network enclaves for servers, industrial controls, SCADA or regulated data. MicroSolved, Inc. (“MSI”) has been helping clients solve information security challenges for nearly twenty-five years on a global scale. In helping our clients segment their networks and protect their traffic flows, we identified a better approach to solving this often untenable problem.

That approach, called MachineTruth™, leverages our proprietary machine learning and data analytics platform to support our industry leading team of experts throughout the process. Our team leverages offline analysis of configuration files, net flow and traffic patterns to simplify the challenge. Instead of manual review by teams of network and systems administrators, MachineTruth takes automated deep dives into the data to provide real insights into how to segment, where to segment, what filtering rules need to be established and how those rules are functioning as they come online.

Our experts then work with your network and security teams, or one of our select MachineTruth Implementation Partners, to guide them through the process of installing and configuring filtering devices, detection tools and applications needed to support the segmentation changes. As the enclaves start to take shape, ongoing oversight is performed by the MSI team, via continual analytics and modeling throughout the segmentation effort. As the data analysis and implementation processes proceed, the controls and rules are optimized and transitioned to steady state maintenance.

Lastly, the MSI team works with the segmentation stakeholders to document, socialize and transfer knowledge to those who will manage and support the newly segmented network and its various enclaves for the long term. This last step is critical to ensuring that the network changes and segmentation initiatives remain in place in the future.

This data-focused, machine learning-based approach enables segmentation for even the most complex of environments. It has been used to successfully save hundreds of man-years of labor and millions of dollars in overhead costs. It has reduced the time to segment international networks from years to months, while significantly raising the quality and security of the new environments. It has accomplished these feats, all while reducing network downtime, outages and potentially dangerous misconfiguration issues.

If your organization is considering or in the process of performing network segmentation for your critical data, you should take a look at the MachineTruth approach from MSI. It could mean the difference between success and struggle for this critical initiative.


Network Segmentation Month

February is Network Segmentation Month at MSI. During February, our blog and social media content will focus on network segmentation initiatives. A how, why, when, what and who –  kind of look at creating secure enclaves within your network.

These enclaves could be based on risk zones, types of systems, types of access, business process, regulatory requirements or many other meta factors. 

We will discuss different reasons for segmenting, approaches to segmentation, some of the lessons we’ve learned from segmenting some of the largest and most complex environments in our 25 year history. It won’t all be positive – we’ll also share some of the ways that segmentation fails, some of the challenges and some of the drawbacks of segmenting networks.

So, strap in and stay tuned for a month of content focused on using segmentation to better secure your environment.

As always, if you have stories to share or want to discuss a specific segmentation question, you can do that via email (info@microsolved.com) or via Twitter to @microsolved or to me personally. (@lbhuston) MSI is always available to help you with segmentation projects, be that planning, implementation, oversight or attestation. We have a proprietary, data-centric approach to this work which we have been using for several years. You can learn more about it here – MachineTruth. We look forward to hearing from you!

Last Quick and Dirty Log Tip for the Week

OK, so this week I posted two other blog posts about doing quick and dirty log analysis and some of the techniques I use. This one also covers converting column logs to CSV.

After the great response, I wanted to drop one last tip for the week. 

Several folks asked me about re-sorting and processing the column-based data in different ways and to achieve different analytical views. 

Let me re-introduce you to my friend and yours, sort.

In this case, instead of using the sort -n -r like before (numeric sort, reverse order), we can use:

  • sort -k# -n input_file (where # is the number of the column you’d like to sort by and the input file is the name of the file to sort)
    • You can use this inline by leveraging the pipe (|) again – i.e.: cat input.txt | sort -k3 -n (this types the input file and sends it to sort for sorting on the third column in numeric order) (-r would of course, reverse it…)
    • You can write the output of this to a file with redirects “> filename.txt”, i.e.: cat input.txt | sort -k3 -n -r > output.txt
      • You could also use “>>” as the redirect in order to create a file if it doesn’t exist OR append to a file if it does exist… i.e..:  cat input.txt | sort -k3 -n -r >> appended_output.txt

That’s it! It’s been a fun week sharing some simple command line processing tips for log files. Drop me a line on Twitter (@lbhuston) and let me know what you used them for, or which ones are your favorite. As always, thanks and have a great weekend! 

Quick And Dirty Log Analysis Followup

Earlier this week, I posted some tips for doing Quick and Dirty PA Firewall Log Analysis.

After I posted this, I got a very common question, and I wanted to answer it here.

The question is something along the lines of “When I use the techniques from your post, the outputs of the commands are column separated data. I need them to be CSV to use with my (tool/SEIM/Aunt Gracie/whatever). How can I convert them?” Sound familiar?

OK, so how do we accomplish this feat of at the command line without all of the workarounds that people posted, and without EVER loading Excel? Thankfully we can use awk again for this.

We can use:

  • awk ‘BEGIN { OFS = “,”} ; {print $1,$2,$3}’
    • Basically, take an input of column data, and print out the columns we want (can be any, in this case I want the first 3 columns), and make the outputs comma delimited.
    • We can just append this to our other command stacks with another pipe (|) to get our output CSV
  • Example: cat log.csv | awk ‘BEGIN { FS = “,”} ; {print $8,$9}’ | sort -n | uniq -c | sort -n -r | awk ‘BEGIN { OFS = “,”} ; {print $1,$2,$3}’
    • In this example, the source IP and destination IP will be analyzed, and the reduced to unique pairs, along with the number of times that that pair is duplicated in the input log (I use this as a “hit rate” as I described earlier
      • A common question, why do I ask for two columns in the first awk and then ask for three columns in the second awk?
        • The answer of course, is that the first awk prints the unique pairs, but it also adds a column of the “hit rate”, so to get the output appropriately, I need all three fields.

So, once again, get to know awk. It is your friend.:)

PS – Yes, I know, there are hundreds of other ways to get this same data, in the same format, using other command line text processing tools. Many may even be less redundant than the commands above. BUT, this is how I did it. I think it makes it easy for people to get started and play with the data. Post your ways to Twitter or share with the community. Exploration is awesome, so it will encourage users to play more. Cool! Hit me on Twitter if you wanna share some or talk more about this approach (@lbhuston).

Thanks for reading!

Quick & Dirty Palo Alto Log Analysis

OK, so I needed to do some quick and dirty traffic analysis on Palo Alto text logs for a project I was working on. The Palo Alto is great and their console tools are nice. Panorama is not too shabby. But, when I need quick and dirty analysis and want to play with data, I dig into the logs. 
 
That said, for my quick analysis, I needed to analyze a bunch of text logs and model the traffic flows. To do that, I used simple command line text processing in Unix (Mac OS, but with tweaks also works in Linux, etc.)
 
I am sharing some of my notes and some of the useful command lines to help others who might be facing a similar need.
 
First, for my project, I made use of the following field #’s in the text analysis, pulled from the log header for sequence:
  • $8 (source IP) 
  • $9 (dest IP)
  • $26 (dest port)
  • $15 (AppID)
  • $32 (bytes)
 
Once, I knew the fields that corresponded to values I wanted to study, I started using the core power of command line text processing. And in this case, the power I needed was:
  • cat
  • grep
    • Including, the ever useful grep -v (inverse grep, show me the lines that don’t match my pattern)
  • awk
    • particularly: awk ‘BEGIN { FS = “,”} ; {print $x, $y}’ which prints specific columns in CSV files 
  • sort
    • sort -n (numeric sort)
    • sort -r (reverse sort, descending)
  • uniq
    • uniq -c (count the numbers of duplicates, used for determining “hit rates” or frequency, etc.)
 
Of course, to learn more about these commands, simply man (command name) and read the details. 😃 
 
OK, so I will get you started, here are a few of the more useful command lines I used for my quick and dirty analysis:
  • cat log.csv | awk ‘BEGIN { FS = “,”} ; {print $8,$9,$26}’ | sort | uniq -c | sort -n -r > hitrate_by_rate.txt
    • this one produces a list of Source IP/Dest IP/Dest Port unique combinations, sorted in descending order by the number of times they appear in the log
  • cat log.csv | awk ‘BEGIN { FS = “,”} ; {print $8,$9}’ | sort -n | uniq -c | sort -n -r > uniqpairs_by_hitrate.txt
    • this one produces a list of the uniq Source & Destination IP addresses, in descending order by how many times they talk to each other in the log (note that their reversed pairings will be separate, if they are present – that is if A talks to B, there will be an entry for that, but if B initiates conversations with A, that will be a separate line in this data set)
  • cat log.csv | awk ‘BEGIN { FS = “,”} ; {print $15}’ | sort | uniq -c | sort -n -r > appID_by_hitrate.txt
    • this one uses the same exact techniques, but now we are looking at what applications have been identified by the firewall, in descending order by number of times that application identifier appears in the log
 
Again, these are simple examples, but you can tweak and expand as you need. This trivial approach to command line text analysis certainly helps with logs and traffic data. You can use those same commands to do a wondrous amount of textual analysis and processing. Learn them, live them, love them. 😃 
 
If you have questions, or want to share some of the ways you use those commands, please drop us a line on Twitter (@microsolved) or hit me up personally for other ideas (@lbhuston). As always, thanks for reading and stay safe out there!