LISA '05 Paper
[LISA '05 Technical Program]
Visualizing NetFlows for Security at Line Speed: The SIFT Tool Suite
William Yurcik - National Center for Supercomputing
Pp. 169-176 of the Proceedings of LISA '05:
Nineteenth Systems Administration Conference,
(San Diego, CA:
USENIX Association, December 2005).
The first step in improving Internet security is measurement -
security events must be made visible. The irony in making this happen
is that there is no lack of security measurement data, in fact, quite
the opposite. However, making security manifest faces a major
challenge: the large volume and multi-dimensional nature of security
data typically obscures valuable security events. NCSA has developed a
suite of tools that solves this problem and is making this software
available to the Internet community.
We present two visualization tools, [Note
1] (1) NVisionIP and (2) VisFlowConnect-IP. Both of these tools
have been developed based on system administrator requirements,
their design peer-reviewed in security research forums, and usability
testing is in process. These tools both present large volume complex
data transparently to system administrators in simple intuitive
visual interfaces that support human cognitive processes. NVisionIP
visually represents the state of all IP addresses on large networks
on a single screen window (we use a Class B address space as the
default) with capabilities to filter and drill-down to subnets and
individual machines for details-on-demand. VisFlowConnect-IP
visually represents flows between internal network IP hosts and the
Internet showing who is connecting with whom with capabilities to
filter and drill-down to subnets and individual machines for
details-on-demand. NVisionIP and VisFlowConnect-IP can be used
individually or in unison for correlating events. This work is
distinguished from others in that these are the first Internet
security visualization tools to be freely available on the Internet
and deployed in large production environments.
Organizations use computer network infrastructures that hold a
vast amount of information for system administrators and security
engineers. There are typical logs common to most computer networks,
but the systems are often large and dynamic, making it difficult to
extract knowledge from the sea of information. Individually, each
system log can be massive, causing operator overload. When overload
occurs, security events can slide by unnoticed. Overload can also
cause operators to disregard alarms due to high false positive rates.
Even in homogeneous infrastructures, solutions from a single vendor
fail to scale to medium or large networks. However, the problem is
compounded because most organizations have network infrastructures
from multiple vendors.
We have developed Security Incident Fusion Tools (SIFT) , an
integrated suite of tools for evaluating the security of an entire
computer network on a single screen. We address the need to discover
security incidents that currently go undetected by security operations
systems. Specifically two SIFT tools, (1) NVisionIP and (2)
VisFlowConnect-IP, leverage human visual cognitive abilities to
process log data into knowledge for situational awareness of network
security. It is estimated that human beings can visually process a
screen of information at 150 Mbits per second , with the ability
to discriminate relatively minor shifts in color, shape, and motion.
By presenting network data visually, it can be scanned quickly,
patterns in complex data rise to the surface, and inferences become
intuitive. Once a security professional becomes familiar with the
normal appearance of the network being monitored, it is much easier to
spot attacks including new so-called "zero-day attacks." The tools
are designed to give security engineers situational awareness of an
entire network in order to help them determine when a network is under
attack, what is being attacked, and what form the attack is taking.
The remainder of the paper is organized as follows: The next
section discusses NetFlows data management and introduces the first
tool in the SIFT suite: CANINE. Subsequently we present the two SIFT
visualization tools - NVisionIP and VisFlowConnect-IPand close with a
summary and on-going future work.
NetFlows Source Data
While this paper focuses on visualization, we would be remiss if
we did not address data management since it is arguably the greatest
obstacle in realizing any scalable visualization system. We address
the challenge of processing high-bandwidth data streams by
instrumenting networks with distributed NetFlows sensors and then
combining this sensor data into a unified format. While in the recent
past NetFlows were solely router-based, PC-based NetFlow sensors
(Argus) make this a feasible solution for most organizations. The
first tool in our suite is a NetFlows converter/anonymizer called
CANINE which can handle different NetFlows formats so independent
implementations can be interoperable with SIFT visualization tools.
NetFlow logs have proven to be the appropriate granularity to process
heavily loaded networks and high bandwidth connections (Gb/s) in near-real-time (five minute monitoring windows).
A network flow is defined as a sequence of packets that are
transferred between two endpoints within a certain time interval. The
endpoints are identified at the network layer by IP addresses and at
the transport layer by port numbers. In addition to data format
differences, there are other interoperability problems in practical
Cisco NetFlows are defined as unidirectional and generated
through intelligent flow cache management, which contains a set of
specialized algorithms .
Argus NetFlows are defined as bidirectional containing two
distinct sub-flows, one in each direction .
Cisco and Argus NetFlow formats have different fields (e.g., flags
etc.) [3, 5].
For a more detailed comparison between different NetFlows formats
With the increased use of NetFlows for security monitoring and the
fact that NetFlows come in different and incompatible formats, we have
developed CANINE (Converter and ANonymizer for Investigating Netflow
Events) [7, 8] which can be downloaded from
CANINE allows tools designed for a specific type of NetFlows to be
interoperable with any NetFlow format. CANINE consists of the two main
modules: (1) the CANINE GUI and (2) the conversion/anonymization
engines. For the purposes of this paper we will only discuss the
conversion engine (for information about the anonymization engine see
[7, 8]). The CANINE GUI accepts user input to identify the NetFlow
file for conversion, sends the request to the processing engine which
performs the conversion to the newly specified output file, and lastly
summarizes the results of the performed actions in a pop-up window. At
present CANINE supports conversion to/from Cisco version 5/7, Argus,
NFdump, and our own NCSA internal NetFlows format. Future formats to
be included in CANINE include Cisco version 9 and the future IETF
With the development of high-speed network infrastructure has also
come the need for high-speed security - security at line speed - for
current 2005 networks this is 4 GB/s at the edge and higher within the
core . Unfortunately, high network bandwidths present special
problems for security monitoring.
The first challenge is the streaming nature of security sensors.
It is important to note that security sensors generate streaming data
and not batch log files. Since streaming analysis is an open research
question, security systems typically create batch log files by
collecting streaming data over defined time periods. However,
depending on the network size and traffic volume these log files can
become large and difficult to handle. Tuning is required to determine
the best time period of analysis to match the preferred log size to
the network size and traffic volume. Creating logs over longer time
intervals may risk losing NetFlows records upon high transmission
rates from overflow or blocking.
The second challenge is observation point. Security cannot be
measured where it is not observed thus sensors need to be placed to
cover the entire network space. Typical deployment for NetFlows
includes the border router for Internet traffic and Argus sensors for
internal network observation. There are blind spots from VLANs and
switched networks which do not leave IP (network layer) traces -
future sensors based on S-Flows are developing to address this gap.
The third challenge is CPU speed to generate and process NetFlows
at line speed. As routers have increased speed, monitoring techniques
have shifted to sampling NetFlows. While sampling is statistically
sufficient for network planning, it is not a good idea for security
analysis. NetFlow records are created by sampling packets (not flows),
letting the majority of the packets go unnoticed, which may lead to
missing important security events. A possible justification for
sampling is that an attack may be high traffic volume, at least part
of which may be captured with high probability (such as high-volume
denial-of-service attack or indiscriminate scanning by propagating
worms and viruses). A preferred approach we recommend for security at
line speed is the parallel processing NetFlows in a distributed
manner. Instead of instrumenting only the high-speed border router
that may only be able generate sampled NetFlows, instead instrument
all the routers feeding into the border router. This technique
effectively relieves the load on each flow collector so that it will
not be over subscribed. The drawback is that multiple flow collectors
are required and NetFlows records from different routers must be
merged to eliminate duplicate flows (the same flow that passes through
NetFlows Visualization Tools
Design By Requirements
We firmly believe that the first step to improve Internet security
is by measurement. Measurement allows one to accurately assess the
degree of the problem at a specified time and then further
measurements track whether solutions are having the desired effect.
However, not all measurements are equal, users have a mental model
based on experience and tools should be designed to enhance and
augment these mental models for the most effective results [16, 17,
For this work, we did two important things often neglected from
security tool design: (1) taking time to work with security engineers
in their operational production environment in order to learn their
mental models and thus tool requirements and (2) the capability to
design new visualization models from scratch to meet these
requirements without having to incorporate legacy constructs. The
results have been very satisfying in that most security engineers who
view our visualization tools for the first time immediately begin
inferring hypotheses based on the content displayed.
To briefly summarize the major findings from our requirements
analysis there are two primary findings. First, security engineers
need to answer questions such as these posed by upper management: What
is the state of the network? Is the network being attacked? How is
the network being attacked? Who is attacking the network? While these
may appear to be basic questions, the answers are not immediately
available using current security tools and when available after much
analysis the answers are complex. Visualization provides a rich
representation to help answer these questions concisely.
Second, security engineers have mental models based on their
experience with the network infrastructure, knowledge of people within
the organization, and security expertise learned over many years.
While most tenets of information visualization design are useful in
designing within our specific security domain, we did find that
leveraging the mental model of security engineers caused us to break
some of these consensus rules (after much consternation). Instances
when the security engineer mental model overrides information
visualization design best practices are highlighted in our discussion
of each of our visualization tools.
Our first and most mature security visualization tool is NVisionIP
[1, 6] which we designed to answer the question: What is the state of
Figure 1 shows the Galaxy view of NVisionIP which can be
downloaded from https://security.ncsa.uiuc.edu/distribution/NVisionIPDownLoad.html. The Galaxy view
represents an entire Class B IP address space (in this single window!)
as a matrix with subnets along the horizontal axis and hosts along the
vertical axis. Each IP address is represented as a dot (actually four
pixels) and the state of each IP address is represented with color or
shape as determined by the user in the color and shape legend. Two
magnification options are available to see the IP addresses: linear
NVisionIP allows the security engineer is to load one (or
multiple) NetFlow files and perform visual queries. NVisionIP has
taken all the possible NetFlow database query combinations and hard
coded them into the tool as drop-down and point-and-click commands. A
user would typically start with primary queries such as how bytes per
IP address or how many connections per IP address. A filter then
allows the user to select secondary queries to view only source or
destination traffic, different protocol (IP, UDP), and different ports
(destination or source ports, specific ports or collections of ports)
or any combination thereof.
At the Galaxy view, NVisionIP can identify large or small levels
of traffic as measured in bytes (based on expectations for the class
of machine - laptop or server). This may indicate malware is being
served to/from a machine or the machine is involved in a denial-of-service
event. Worm and virus scans as indicated by number of
connections can also be easily detected based on variance from
There are aspects of the Galaxy view design that are contrary to
information visualization best practices: the IP address space is laid
out logically in matrix space without organizing IP addresses into
known classes or enlarging the part of the IP address space with more
activity (thus patches of white space or inactive IP address space
appears). This design was intentional to retain security engineer
knowledge of the IP address space based on logical numbering for
subnets/hosts and mental mapping between logical addresses (e.g.,
cluster compute nodes with contiguous IP addresses) and physical
locations (IP subnets are usually physically located in the same area
such as a building floor etc.). The white space of inactive IP
addresses actually has other advantages and is not wasted space - any
traffic activity shown there is anomalous (unallocated address space
that should have no legitimate traffic).
While an overall view is important, it is of limited use without
the ability to drill down to find more detailed information when
something interesting is identified. Figure 2 shows the drill-down
levels of NVisionIP which are activated with a mouse click and a drag
over a region of interest. These levels are the (1) Small Multiple
View and (2) Machine View.
The Small Multiple View allows the user to quickly scan and
compare traffic activity across subnets on many machines
simultaneously. Each machine is a box with two sets of histograms, an
upper set of histograms representing traffic on well-known ports and a
lower set of histograms representing traffic on ports over 1024. The
well-known ports are color-coded in a user legend. The ports over 1024
are ordered from most active to least active (top N ports). Note that
no numbers are shown in the small multiple view, this view is designed
for the user to identify activity of interest and then drill-down for
raw data details on-demand.
1: NVisionIP Galaxy view of an entire Class B IP address
2: The three levels of NVisionIP (top to bottom): (1) Machine view, (2)
Small multiple view, and (3) Galaxy view.
3: VisFlowConnect-IP: Main view.
4: Drill-down layers of VisFlowConnect-IP: left Domain View and Bright
At the Small Multiple View, NVisionIP has been used to quickly identify
anomalous services that violate security policy such as unauthorized services
(web or mail server) and exposed services that need to be patched or otherwise
protected due to vulnerabilities.
If a user sees a machine with interesting traffic activity in the Small
Multiple View, they may click on that block to drill-down to the Machine View.
The Machine View organizes all the data from a particular machine in multiple
tabs - each tab showing aggregate activity in an upper histogram and
source/destination directional flows in two lower histograms. Note in the
Machine View each histogram is fully labeled with port numbers and traffic
level. At the lowest level, the raw NetFlows data for that machine is
available for inspection in this Machine View as a tab. At this lowest level
Machine View, details of most security events can be revealed.
The overall effect of using NVisionIP (with its interactive drill-down
levels) is that relationships between aggregate network activity and
individual machine activity can be more easily discovered and comprehended by
human operators - providing situational awareness of network system state.
Future work is progressing on optimizing Galaxy View animation to show IP
address state changes over time as well as a difference view to visually
compare current network traffic versus benchmark network traffic.
The second security visualization tool in the SIFT tool suite focuses on
answering the question: Who is connecting to whom on the network? This basic
question has been attempted in the past with topology-based diagrams based on
network infrastructure, however, these results were either (1) not providing
information relevant to real-time traffic or (2) not scalable since
representing network traffic over time produces obscured lines in relatively
short order. We solve both of these problems with VisFlowConnect-IP [12, 13,
14] which is available at https://security.ncsa.uiuc.edu/distribution/VisFlowConnectDownLoad.html.
VisFlowConnect-IP is a security visualization tool based on the parallel
axes concept drawn from data mining. It is a complementary tool to NVisionIP
since it visualizes the same NetFlows source data - the design similarities
will become apparent in the following description. VisFlowConnect-IP allows a
user to visually assess the connectivity of large and complex networks (in a
single window!) by providing a main view of the network with filter and
drill-down views that provide more details on-demand. The three views of
VisFlowConnect-IP are: (1) Main, (2) Domain, and (3) Internal. The Main View
is shown in Figure 3 with the Domain and Internal views shown in Figure 4.
The VisFlowConnect-IP Main View utilizes the parallel axis view with the
left-most and right-most vertical axes representing the external domains and
the center vertical axis representing host IP addresses within the internal
edge network domain (See Figure 3). Lines connecting external domains and
internal hosts represent directional data flows, with line darkness being
proportional to the logarithm of the volume of data transferred.
VisFlowConnect-IP can filter/highlight flows to certain hosts or traffic on
specific ports and protocols using a filter drop-down menu and selection boxes
on the main view. Ports indicated in the selection boxes are represented in
different colors within the network traffic or may be isolated from network
traffic for focused analysis. The overall effect is visualization of traffic
into-an-edge-network-from-the-Internet and traffic out-from-an-edge-network-to-the-Internet.
Figure 4 shows the two drill-down views within VisFlowConnect-IP. While we
would have liked to represent each individual external host IP address
connecting into the internal edge network symmetrically on both the left-most
and right-most axes, this is not possible due to scalability. Preliminary
measurements of NCSA's network showed over 100,000 different IP addresses
commonly appeared in the NetFlow files we wished to visualize and this is too
many for the vertical line pixel space of a single window without scrolling.
Instead we implemented a drill-down Domain View which is invoked by the user
clicking on a drop-down menu while having an external domain highlighted on
the vertical external domain axis. The resulting Domain View is a mirror image
of the Main View except it only shows traffic within the highlighted external
network domain to/from the internal edge network. This has turned out to be
very valuable since typically hackers "own" entire subnets or even "own"
entire network domains so it is common to see malicious activity captured
within a Domain View.
Figure 4 also shows the drill-down Internal View which is invoked as a
toggle button on the Main View. While monitoring for external Internet hacker
activity is sexy, we have found this Internal View very useful since it shows
only traffic that both sources and sinks within the internal edge network.
There are only two vertical lines in this view, internal edge network IP
addresses are ordered symmetrically in a mirror image on the left-most and
right-most axes (no middle axis). This Internal View has helped security
engineers determine important security events like the initial source of a
worm infection which infiltrated the edge network from the inside, and the
insider attacks from those misusing privileged access.
The VisFlowConnect-IP Main View has a time axis at the bottom which is
used to solve the scalability problem we referred to as the major challenge
for this tool. The user loads a NetFlow file for visualization and then may
select multiple filters to determine how this traffic is to be represented in
animation including intensity, byte size, and a sliding time window. The
sliding time window provides scalability by only representing traffic within
the window and ignoring traffic outside the window. Thus the sliding time
window can be adjusted to any size network and any traffic volume - the
general rule for clear viewing is the more traffic the smaller the sliding
time window. The window size itself is represented to the user by a red box
(where the length of the red box is proportional to window size) that travels
along the time axis as the traffic is animated (as shown in the Domain and
Internal Views within Figure 4).
VisFlowConnect-IP has also implemented a filter language using real
expressions that is beyond the scope of this paper . With this filter
language capability, VisFlowConnect-IP can create mechanisms for
storing/retrieving filter profiles. These profiles can store customized
filters that remove "uninteresting" information from view-thus leaving only
the more security relevant data to be displayed.
Visualization is the future of security monitoring and NetFlows are the
source data for high-speed networks. In this paper we marry security
visualization with NetFlows by presenting the SIFT suite of tools along with
accompanying techniques for security at line speed. The goal is to enable
security engineers to go beyond binary/text command line log file analysis
toward real-time network security situational awareness. A growing community
of researchers has formed on security visualization, see  for more
The three specific tools of the SIFT suite presented in this paper
(CANINE, NVisionIP, and VisFlowConnect-IP) are available for download at the
URLs provided in the text. We are currently conducting usability tests with
human subjects to quantify the utility of these tools and preliminary results
from these tests are very promising. We intend to go open source with these
tools after the software is stable, at present we are still developing the
software with new versions posted on the corresponding webpages. We
enthusiastically invite feedback from users about the use of these tools.
William (Bill) Yurcik is currently Manager, Security R&D and Senior
Systems Security Engineer at NCSA. Prior to this he was Head of Security
Operations at NCSA, so he has both a theoretical and practical background in
computer network security. Prior to joining NCSA he has 12 years of
professional experience as a Network Engineer for large networks (Naval
Research Laboratory, NASA, Verizon, and MITRE). He is a graduate of Johns
Hopkins University (MS Electrical Engineering 1990, MS Computer Science 1987),
the University of Maryland (BS Electrical Engineering 1984), and is Ph.D. ABD
from the University of Pittsburgh (1994-99). Bill can be reached at
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Note 1: Funded in part by grants
from the Office of Naval Research (ONR) under the auspices of the
Technology Research, Education, and Commercialization Center
(TRECC) and the National Center for Advanced Secure Systems
Research (NCASSR) both established at NCSA/University of