Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Attacks/Breaches

1/3/2014
08:42 PM
Connect Directly
Twitter
RSS
E-Mail
50%
50%

Network Baseline Information Key To Detecting Anomalies

Establishing 'normal' behaviors, traffics, and patterns across the network makes it easier to spot previously unknown bad behavior

While so much time in network security is spent discussing the discovery of anomalies that can indicate attack, one thing that sometimes gets forgotten in the mix is how fundamental it is to first understand what "normal" looks like. Establishing baseline data for normal traffic activity and standard configuration for network devices can go a long way toward helping security analysts spot potential problems, experts say.

"There are so many distinct activities in today's networks with a high amount of variance that it is extremely difficult to discover security issues without understanding what normal looks like," says Seth Goldhammer, director of product management for LogRhythm.

Wolfgang Kandek, CTO of Qualys, agrees, stating that when IT organizations establish baseline data, it makes it easier to track deviations from that baseline.

"For example, if one knows that the use of dynamic DNS services is at a low 0.5 percent of normal DNS traffic, an increase to 5 percent is an anomaly that should be investigated and might well lead to the detection of a malware infection," Kandek says.

[Are you using your human sensors? See Using The Human Perimeter To Detect Outside Attacks.]

But according to Goldhammer, simply understanding normal can be a challenge in its own right. Baselining activities can mean tracking many different attributes across multiple dimensions, he says, which means understanding normal host behavior, network behavior, user behavior, and application behavior, along with other internal information, such as the function and vulnerability state of the host. Additionally, external context -- such as reputation of IP -- plays a factor.

"For example, on any given host, that means understanding which processes and services are running, which users access the host, how often, [and] what files, databases, and/or applications do these users access," he says. "On the network [it means] which hosts communicate to which other hosts, what application traffic is generated, and how much traffic is generated."

It's a hard slog, and, unfortunately, the open nature of Internet traffic and diverging user behavior make it hard to come up with cookie-cutter baseline recommendations for any organization, experts say.

"Networks, in essence, serve the needs of their users. Users are unique individuals and express their different tastes, preferences, and work styles in the way they interact with the network," says Andrew Brandt, director of threat research for the advanced threat protection group for Blue Coat Systems. "The collection of metadata about those preferences can act like a fingerprint of that network. And each network fingerprint is going to be as unique as its users who generate the traffic."

Another added dimension to developing baseline is time. The time range for sampling data for establishment of a benchmark will often depend on what kind of abnormality the organization hopes to eventually discover.

"For example, if I am interested in detecting abnormal file access, I would want a longer benchmark period building a histogram of file accesses per user over the previous week to compare to current week, whereas if I want to monitor the number of authentication successes and failures to production systems, I may only need to benchmark the previous day compare to the current day," Goldhammer says.

While baselines can be useful for detecting deviations, TK Keanini, CTO of Lancope, warns that it may actually be useful to think in terms of pattern contrasts rather than "normal" and "abnormal."

"The term 'anomaly' is used a lot because people think of pattern A as normal and patterns not A as the anomaly, but I prefer just thinking about it as a contrast between patterns," Keanini says. "Especially as we develop advanced analytics for big data, the general function of 'data contrasts' deliver emergent insights."

This kind of analysis also makes it less easy to fall prey to adversaries who understand how baselines can be used to track deviations. Instead of a single, static baseline, advanced organizations will constantly track patterns and look for contrasts across time.

"The adversary will always try to understand the target norms because this allows them to evade detection," he says. "Think about how hard you make it for the adversary when you establish your own enterprise wide norms and change them on a regular basis."

However it is done, when a contrast of patterns does flag those tell-tale anomalies, Kandek recommends that immediate analytical response should be organized.

"To deal with network anomalies, IT departments can lean on a scaled-down version their incident response process," he says. "Have a team in place to investigate the anomalies, document the findings, and take the appropriate actions, including adapting the baselines or escalating to a full-blown incident response action plan."

Foremost in that immediate action is information-sharing, Brandt recommends.

"When you identify the appropriate parameters needed to classify traffic from the "unknown" to the "known bad" column, it's important to share that information, first internally to lock down your own network, and then more widely, so others might learn how they can detect anything similar on their own networks," he says.

Have a comment on this story? Please click "Add Your Comment" below. If you'd like to contact Dark Reading's editors directly, send us a message. Ericka Chickowski specializes in coverage of information technology and business innovation. She has focused on information security for the better part of a decade and regularly writes about the security industry as a contributor to Dark Reading.  View Full Bio

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Commentary
How SolarWinds Busted Up Our Assumptions About Code Signing
Dr. Jethro Beekman, Technical Director,  3/3/2021
News
'ObliqueRAT' Now Hides Behind Images on Compromised Websites
Jai Vijayan, Contributing Writer,  3/2/2021
News
Attackers Turn Struggling Software Projects Into Trojan Horses
Robert Lemos, Contributing Writer,  2/26/2021
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Write a Caption, Win an Amazon Gift Card! Click Here
Latest Comment: This comment is waiting for review by our moderators.
Current Issue
2021 Top Enterprise IT Trends
We've identified the key trends that are poised to impact the IT landscape in 2021. Find out why they're important and how they will affect you today!
Flash Poll
How Enterprises are Developing Secure Applications
How Enterprises are Developing Secure Applications
Recent breaches of third-party apps are driving many organizations to think harder about the security of their off-the-shelf software as they continue to move left in secure software development practices.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2021-21331
PUBLISHED: 2021-03-03
The Java client for the Datadog API before version 1.0.0-beta.9 has a local information disclosure of sensitive information downloaded via the API using the API Client. The Datadog API is executed on a unix-like system with multiple users. The API is used to download a file containing sensitive info...
CVE-2021-27940
PUBLISHED: 2021-03-03
resources/public/js/orchestrator.js in openark orchestrator before 3.2.4 allows XSS via the orchestrator-msg parameter.
CVE-2021-21312
PUBLISHED: 2021-03-03
GLPI is open source software which stands for Gestionnaire Libre de Parc Informatique and it is a Free Asset and IT Management Software package. In GLPI before verison 9.5.4, there is a vulnerability within the document upload function (Home > Management > Documents > Add, or /front/documen...
CVE-2021-21313
PUBLISHED: 2021-03-03
GLPI is open source software which stands for Gestionnaire Libre de Parc Informatique and it is a Free Asset and IT Management Software package. In GLPI before verison 9.5.4, there is a vulnerability in the /ajax/common.tabs.php endpoint, indeed, at least two parameters _target and id are not proper...
CVE-2021-21314
PUBLISHED: 2021-03-03
GLPI is open source software which stands for Gestionnaire Libre de Parc Informatique and it is a Free Asset and IT Management Software package. In GLPI before verison 9.5.4, there is an XSS vulnerability involving a logged in user while updating a ticket.