Ourmon and Network Monitoring Performance

James R. Binkley Bart Massey Computer Science Dept. Portland State University

Portland, OR, USA {jrb,bart}@cs.pdx.edu

Abstract

Ourmon is an open-source network management and anomaly detection system that has been developed over a period of several years at Portland State University. Ourmon monitors a target network both to highlight abnormal network traffic and measure normal traffic loads. In this paper, we describe the features and performance characteristics of Ourmon.

Ourmon features include a novel mechanism for running multiple concurrent Berkeley Packet Filter (BPF) expressions bound to a single RRDTOOL-style graph, as well as various types of ”top talker” (top-N) filters aimed at conventional network flow measurements and anomaly detection. These features permit a variety of useful and easily-understood measurements.

One problem that sniffer-based network monitor systems face is network-intensive attacks that can overwhelm monitoring and analysis resources. Lab experiments with an IXIA high-speed packet generator, as well as experiences with Ourmon in a real network environment, demonstrate this problem. Some recent modifications to Ourmon have greatly improved its performance. However, minimum-size packets in a high-speed network can still easily make a host lose packets even at relatively slow rates and low monitor workloads. We contend that small packet performance is a general network security problem faced by current monitoring systems including both open source systems such as Ourmon and Snort, and commercial systems.

1 Introduction

The Ourmon [15] network monitoring system is an open-source tool for real-time monitoring and measurement of traffic characteristics of a computer network. It runs on FreeBSD, and Linux. (There is also code for Solaris, although it is currently unmaintained.) Ourmon’s feature set includes various top talker filters and multiple instances of the Berkeley Packet Filter (BPF) which taken together allow us to capture interesting features of the envelope of incoming IP packets. Network monitoring and data visualization are typically performed on separate hosts. The data visualization system uses standard network graphical tools to display the resulting measurements in a fashion that highlights anomalies.

The Internet has recently faced an increasing number of bandwidth-intensive Denial-Of-Service (DOS) attacks. For example, in January 2003 the Slammer worm [4, 12] caused serious disruption. Slammer not only wasted bandwidth and affected reachability, but also seriously impacted the core routing infrastructure. At Portland State University (PSU), four lab servers with 100 Mb NIC cards were infected simultaneously. These servers then sent approximately 360 Mb/s of small packets to random destinations outside of PSU. This attack clogged PSU’s external connection to the Internet, in the process also causing important network monitoring failures. Due to the semi-random nature of the IP destination addresses generated by the worm, the CPU utilization of a router sitting between network engineers and network instrumentation rose to 100%. Engineers were thus cut off from central network instrumentation at the start of the attack.

We recently acquired an IXIA 1600 high-speed packet generator. The Slammer attack inspired us to test our Ourmon network monitoring system against a set of Gigabit Ethernet (GigE) flows. Our test flows included maximum-sized (1518 byte) and minimum-sized (64 byte) UDP packets, with both fixed and rolling IP destination addresses.

The Ourmon network measurement system architecture consists of two parts: a front-end probe and a backend graphics engine system. Optimally these two parts should run on two separate computers in order to minimize the application compute load on the probe itself. Our goal in these experiments has been to test the performance of our probe and its BPF network tap rather than the back-end system.

We constructed a test system consisting of: the IXIA with two GigE ports; a line speed GigE switch capable of port-mirroring; and a FreeBSD workstation with a GigE NIC card. The IXIA was set up to send packets from one GigE port to the other. The switch was set up to mirror packets from one IXIA port to the UNIX host running our front-end probe.

Like other tools including tcpdump [19], Snort[16], or Ntop [5, 14], the Ourmon front-end uses the BPF as a packet tap. The application takes a stream of unfiltered packets directly from a BPF kernel buffer fed by an Ethernet device, bypassing the host TCP/IP stack. The interface interrupts on packet input, and hands the trimmed packet (containing all headers through layer 4) to the kernel BPF filter buffer. The Ourmon probe application reads packets, subjecting each packet in turn to a set of configuration filters. It thus makes sense to separately test the performance of the BPF and the performance of the Ourmon probe filter system.

Our experimental questions include the following:

1. Using GigE with maximum or minimum-sized packets, at what bit rate can the underlying packet tap and buffer system successfully process all packets?

2. Using GigE with maximum or minimum-sized packets, what is the smallest BPF kernel buffer size (if any) for which all packets are successfully processed?

3. Ourmon has three kinds of filters: hardwired C filters, BPF-based interpreted filters, and a “top-N” flow analysis system (one of a set of top-N tuple types). Can we determine anything about the relative performance of these filters? If we are receiving a high number of packets per second, which of these filters can keep up?

4. With the Slammer worm, we know that semirandom IP destinations led to inefficient route caching in intermediary routers. What happens when we subject our top-N flow filter to rolling or semi-random IP destinations?

In section 2 we provide a short introduction to the Ourmon system. In section 3 we discuss our test setup. In section 4 we present test results. In section 5 we discuss possible means for improving our performance, including several small-scale application optimizations that have shown reasonable performance improvements. In section 6 we present related work. Section 7 contains a discussion of problems and future work. Section 8 draws some brief conclusions.

2 Introduction to Ourmon

Our measurement work in subsequent sections focuses on the Ourmon probe rather than the graphics engine. However, in this section we give an overview of the complete Ourmon system. This serves to introduce Ourmon and to describe the basis of the measurement effort. To further these goals, we discuss the system at a high-level, introducing the basic feature sets which we call filters.

Figure 1: Ourmon network setup

We provide some probe configuration examples and a few sample visualizations. All visualizations have been taken from actual data produced from the Ourmon probe stationed in PSU’s “network center” or DMZ. Ourmon is a complex system. While detailed workings of every feature of the Ourmon system are outside the scope of this paper, we attempt to give an overall understanding of system operation.

2.1 Architecture

Ourmon is a “near” real-time web-based network monitor. Web-based data never lags reality by greater than one minute. The system (and its name) are inspired by SNMP RMON [22] monitors. The Ourmon probe assumes the port-mirroring functionality of Ethernet-based switches. A typical setup may be seen in Figure 1. Ourmon can be configured in many ways. At PSU, the Ourmon probe is placed in an internet gateway network so that we can see all traffic going to and from the Internet. In addition, within the PSU Maseeh College of Engineering and Computer Science, we use an Ethernet switch and set Ourmon up to watch important server traffic. An Ethernet switch is configured to mirror (duplicate) packets sent to its Internet connection on port 1. All packets received via the Internet port are copied to port 3, which is running the front-end Ourmon probe on a FreeBSD system using the BPF packet tap. Thus the probe setup is similar to that of Snort, which we show running on port 2 of the switch. The back-end graphics engine is not performance critical. It may run on a second computer, which need not be exposed to the Internet.

The probe, written in C, has an input configuration file and a main output statistics file. (Depending on the features used, other output files are possible.) The configuration file, ourmon.conf, specifies various named filters for the probe to use. Typically probe output is written to a small ASCII file, mon.lite, that summarizes the last 30 seconds of filter activity in terms of statistics recorded by each configured filter. Ourmon takes copious packet data and tries to summarize it in a statistical way, typically producing integers bound to BPF-based filters or lists of tuples. Tuples may be top-N flows or other top-N tuples typically keyed to an IP source “host”. The goal is to produce small amounts of heavily summarized output data. All probe inputs and outputs are in simply-formatted ASCII, facilitating analysis and toolbased processing. If the graphics-engine is on a separate computer, the resulting output files may be copied over the network to that box. The graphics engine, in turn, produces various graphic outputs and ASCII reports for web display. This file transfer is a simple task, accomplished using standard UNIX tools. One typically uses a batch ssh script driven by crontab to accomplish the transfer by pulling data from the probe. Other file transfer programs including rsync, wget, or even NFS will also work. The probe is protected from unauthorized access via a host-based access control list.

The graphics engine, written in Perl, produces several kinds of graphics and reports. RRDTOOL-based [17] strip charts are used with BPF filter-sets and hardwired filters. A filter-set is a set of BPF expressions bound to a single RRDTOOL graph. RRDTOOL graphs are wrapped in HTML web pages to ease access. Web pages constituting a year of baselined data are available via the RRD logging system. Histograms and reports are used to display the top-N flow filter and other similar ”top talker” tuple lists. A variety of logging is performed: raw logging of probe output; individual logs per top-N tuple; and report summarizers for some of the more interesting tuples. The resulting reports provide both hourly summaries for the current day, and summary data for the last week. Ourmon does not include a web server (we typically use apache). Our basic install provides a default installation of BPF-based filter-sets and top talker web pages, with individual web pages per filter, as well as a top-level web page for accessing the data.

Figure 2 shows the overall Ourmon system architecture. Upon installation, our configuration utility creates probe and graphics engine shell scripts. In the back-end graphic box, it also installs a set of static web pages which encapsulate runtime generated graphics and reports. The probe is started and stopped via its shell script and runs as a background daemon. It typically runs at boot. It takes the ourmon.conf input file, parses it, and

Figure 2: Ourmon software architecture

then reads packets from the command-line specified network device. It outputs various files depending upon configured filters including the primary mon.lite file and other optional secondary tuple files. The graphics engine script is responsible for transferring the raw probe data files to the display machine (if necessary), and invokes analysis tools to produce graphics for the web interface and also produces log information. The graphics engine script is invoked by cron once per minute and actually runs to completion twice per minute – hence the sample period is thirty seconds. The graphics engine places the graphics and some analyzed report data in the web output directory thus refreshing the data encapsulated by the web pages installed at configuration time. The script here also produces logging information not available on the web, which may be looked at for layer analysis. The user interface for the resulting display of data is the user’s web browser of choice.

The Ourmon web interface is quite simple. Ourmon can be observed in operation in the PSU DMZ by

                                              visiting http://ourmon.cat.pdx.edu/ourmon

with a graphical web browser. From there, a variety of RRDTOOL and other filter outputs are available. More reports can be accessed simply by clicking on the appropriate links. The top-level web page in general shows current RRDTOOL pictures, current histogram pictures for top-N data and also has links to some kinds of sample period or summarized daily reports. The top page may be viewed as a directory for the various RRDTOOL filters, and other kinds of filters. This is because it is intended that there should be at least one current picture for every filter type on the top level page. The top picture may in turn lead to a link that provides more filter details on lower level pages. If we focus on RRDTOOL filters, each filter has its current graph for today on the top-level page with ”now” on the right-hand edge of the picture. That graph in turn is a link that leads to a second-level page that has graphs for the current day (roughly), the current week, the current month, and the current year. Thus each RRDTOOL filter provides a year’s worth of baselined data (this is a typical RRDTOOL toolset feature and is not a noteworthy Ourmon feature). Top-N data is similar. The current picture for the top 10 tuples is shown on the top page, and that picture in turn is a link to a second-level page that provides supplemental tuples. Because the current Ourmon front page is quite large, it is not practical to provide a screenshot. However, the displays shown in this paper give a good indication of the kinds of graphical display available.

2.2 Configuration and Use

The Ourmon probe process uses the BPF in two ways. The BPF library is used to extract packets from the kernel BPF buffer system. Ourmon also allows the administrator to evaluate multiple concurrent BPF expressions in user mode. In the probe configuration file, a user can group logically related BPF expressions in a BPF filterset. Each expression in the set can be graphed as a separate line in a shared RRDTOOL strip chart graph in the back-end. Such filter-sets have a name, provided by the user in the front-end config. The back-end uses the filter name to synthesize an RRDTOOL database expression, and to create runtime graphics for any new filterset. We provide a collection of BPF filter-sets with our default install, many of which perform useful network anomaly detection tasks. It is also easy to configure new graphical outputs for Ourmon data. Graphical outputs are described using the ”tcpdump” expression language as found in libpcap [19]. Tcpdump and other common network tools also use these filters. After creating a new probe configuration filter, the user must give it a unique name. A small amount of HTML, copied from our template files, is usually sufficient to glue the new filter to the supplied main page. We hope to further automate this process in a future release. Thus Ourmon may be easily extended by a user with a new BPF-based set of expressions.

As one example, Figure 3 shows a simplified probe configuration for one BPF filter-set. This filter-set groups the performance of five application services together and uses one BPF expression each for ssh, combined P2P protocols, web, FTP, and email. Probe output is not intended for human consumption, but is useful for debugging. The probe output for the filter above over a snapshot period might look like this:

                                                      bpf "ports" "ssh" "tcp port 22"
                                                      bpf-next "p2p" "tcp port 1241 or
                                                           tcp port 6881"
                                                      bpf-next "web" "tcp port 80 or
                                                           tcp port 443"
                                                      bpf-next "ftp" "tcp port 20 or
                                                           tcp port 21"
                                                      bpf-next "email" "tcp port 25"

Figure 3: Ourmon probe configuration

                                                      bpf:ports:5:ssh:254153:p2p:19371519:
                                                       web:41028782:ftp:32941:email:1157835

Thus ssh/p2p/web/ftp/email byte counts will all appear in the same RRDTOOL graph as in Figure 4.

The filter configuration allows the user to name the composite filter-set graph “ports”. This is accomplished using the “bpf” configuration tag. This tag provides a line label and initial expression for the graph. The configuration tag “bpf-next” adds another BPF expression to the graph. The graph may be terminated one of several ways, including a new “bpf” tag, which starts a new graph. Overall, five separate user-mode BPF configuration expressions like “tcp port 22” are mapped to appropriate line labels (“ssh”) in the same graph. (This graph is taken from the PSU DMZ and shows web traffic and P2P traffic as the biggest bandwidth consumers.) The probe executes the user-mode BPF runtime expressions on the incoming packet stream from the packet tap, counting matching bytes or packets. At the sample period timeout, it outputs the mon.lite file. In this case, the file includes the name of the filter-set and line label / byte count tuples for each BPF expression. Note that multiple BPF filter-sets are possible. Thus many separate BPF expressions can be executed in the probe application. At the time of writing, the current PSU DMZ probe software is running around 80 BPF expressions in twenty filter-sets.

Ourmon also supports a small set of “hardwired” filters programmed in C and turned on via special filter names in the configuration file. For example, a hardwired filter counts packets according to layer 2 unicast, multicast, or broadcast destination address types. One very important filter called the packet capture filter includes statistics on dropped and counted packets provided directly from the BPF kernel code. The packet capture filter is fundamental. It is used to determine when the kernel BPF mechanism plus application mix is overloaded in our testing. Typical front-end output in the mon.lite file for that filter and the layer 2 packet address type filter might look like this:

                                                      pkts: caught 53420 drops: 0
                                                      fixed_cast: mcast: 2337215:
                                                       unicast: 15691896: bcast: 0:

Figure 4: A BPF filter-set graph showing application byte counts

The packet capture filter (“pkts”) output shows that the packet tap during the last sample period caught 53240 packets and dropped none. In Figure 5 we show an example back-end graph for this filter. The upper line in the figure indicates captured packets: the lower line indicates drops. This graph is from our DMZ during the day of a Slammer re-infection. It can be seen that the Ourmon probe, at the time running on a Pentium-3, has caught the attack even though many packets have been dropped. This is a real-world example of small packets causing a monitor system to underperform. We have also seen distributed TCP SYN attacks cause the same phenomenon.

The third and last filter class in Ourmon are top-N based filters. Top-N filters produce a sublist of limited set size: the top-N elements of the entire sorted list. The list is characterized by a tuple that includes a key and a set of integer counters. In some cases a nested list of tuples might include sampled destination ports or other data. Tuple keys may be as simple as an IP source address. This has proven to be a very profitable focus for network summarization and anomaly detection. An IP flow from the top-N flow filter can also be a tuple key. Tuple-based filters currently include:

1. The traditional top-N talker flow filter that tells us the top IP, TCP, UDP, and ICMP flows. We view this filter as typical of its class: in this paper we focus only on measurements related to this filter.

2. A top-N port monitor that tells us which TCP and UDP ports are the most used.

3. A top-N TCP SYN monitor that is quite useful in anomaly detection. It includes a basic top talker graph to tell us which IP hosts are sending the most SYNS. It also includes several important anomaly detection functions including a port signature report that reveals ”noisy” hosts with a set of sampled destination ports. These hosts are typically P2P systems or scanners or hosts infested with worms. An RRDTOOL-based worm graph gives us a count of such noisy hosts at any one time. This SYN tuple filter is the focus of much ongoing research.

4. A top-N scanning monitor that tells us which hosts are doing IP destination scanning, and which hosts are doing L4 (TCP and UDP) destination port scanning.

5. A top-N ICMP error monitor that tells us which hosts are generating the most ICMP errors and as a side effect, which systems have created the most ICMP errors with UDP packets.

When configured to use the top-N flow filter, the probe builds up a hash-sorted list of IP flows over the sample period and writes the top-N, say 10 to 100, IP flows to the main output file. It also writes subsets of the IP flow including TCP, UDP, and ICMP flows. The graphics-engine takes this information and produces graphical histograms and text report summaries hourly. The key for this tuple type is a flow. A flow is a five-tuple consisting of IP source, IP destination, IP next protocol, L4 source port, and L4 destination port. See Figure 6 for an example of back-end graphics for the top-N report: we show a DOS attack on a local IT administrator’s host machine. The top six flows in the graph con-

Figure 5: The packet capture filter graph showing counts and drops during a slammer attack

stitute the attack. The attack packets were launched over Internet2 using a spoofed IP source address and unfortunately clogged our Internet1 connection (a DS-3 at the time). Multiple UDP flows, each around 1.5 Mb/s, are shown. This picture is of historic significance to us as it was discovered during a “demo” on the first day that Ourmon was ever deployed in PSU’s network. This result emphasized to us the fact that Ourmon is not just a networking monitoring system, but also an anomaly detection system.

In summary, the front-end has three kinds of filters: hardwired C filters, user configurable BPF filter-sets, and top-N tuples including a flow filter. We are interested in the execution cost of each of these three kinds of filters. Of our tuple types, we have chosen the top-N flow filter as representative of its class. It is also the first filter type developed in its class so we thus have the most experience with it. A user may program any number of BPF filter-sets and this complicated the analysis somewhat.

The packet capture filter is especially important, as it serves to tell us when we are losing packets. We can view this as an important indicator that the combined kernel and probe application system is in failure mode. An important cause of failure is too much work done at the application layer, causing the application to fail to read buffered kernel packets in a timely manner.

3 Experimental Setup

The hardware used in our testing consists of three pieces of equipment:

1. An IXIA 1600 chassis-based packet generator with a two port GigE line card. One port sends packets

and the other port receives packets.

2. A Packet Engines line speed GigE switch. Three ports on the switch are used: one for the IXIA send port, one for the IXIA receive port, and a third port connected to the UNIX host for mirroring the IXIA flow.

3. A 1.7 GHz AMD 2000 computer system. The AMD processor is roughly comparable to a 2GHz Intel Pentium 4 processor. The system motherboard is a Tyan Tiger MPX S2466N-4M. The motherboard has two 64-bit PCI slots. We use a SysKonnect SK-9843 SX GigE card in one of the slots.

Software used includes Ourmon 2.0 and 2.4, (2.4 at the time of writing), along with the 0.7.2 libpcap [19] library. The host operating system is FreeBSD 4.9, running only the Ourmon front-end probe.

We set up the IXIA to send either minimum-sized packets or maximum-sized Ethernet packets. One port on the IXIA sent packets through the switch to the other IXIA port. All packets were UDP packets.

The IXIA allows the user to select an arbitrary packet sending rate up to the maximum possible rate. It can also auto-increment IP destination addresses. We used this feature as an additional test against the top-N filter. According to Peterson [7] the maximum and minimum theoretical packet rates for GigE are as shown in Table 1. We used these values as a measurement baseline. We observed that the IXIA 1600 can indeed generate packets at nearly 100% of this rate for both maximum and minimum-sized packets. We used these numbers to make sure that our Ethernet switch did not drop packets. We hooked both IXIA GigE ports up directly to the switch and sent packets from one IXIA port to another.

Figure 6: Top-N UDP flow histogram showing a DOS attack

Table 1: GigE rates

B/pkt pkt/s
min  64 1488000
max 1518  81300

The IXIA’s built-in counters at the receive port reported the same packet counts as at the send port.

The test methodology involves setting up a UNIX host with a driver script and some set of Ourmon filters. The front-end probe is started, and the IXIA is configured to send min or max packets at some fraction of the maximal rate. Ourmon is configured with some combination of hardwired, user-mode BPF, and the top-N filter as desired. The test flows are then started on the IXIA, and the results observed using the mon.lite output file.

The test script is the Bash Shell script shown in Figure 7. The FreeBSD sysctl(8) command is used to set the kernel BPF buffer size. This is because recent versions of the pcap(3) library on FreeBSD will automatically size the buffer to be used by the client application to match the kernel buffer size. It should be noted that the traditional size of the kernel BPF buffer is typically small (a few KB/s), as it was originally intended for the tcpdump sniffer. The parameters to the Ourmon probe program tell it to take input from a local configuration

                                                      #!/bin/sh
                                                      BSIZE=1048576
                                                      sysctl -w debug.bpf_bufsize=$BSIZE
                                                      sysctl -w debug.bpf_maxbufsize=$BSIZE
                                                      ./ourmon -a 5 -I sk0 -m /dev/tty \
                                                        -f ./ourmon.conf

Figure 7: Test script

file, to dump the output information to the screen every five seconds, and to use the SysKonnect card as the input interface.

Tests were run using either maximum-sized or minimum-sized packets. If we dropped packets, we attempted in every case to eliminate packet drops by increasing the kernel BPF buffer size (BSIZE above). If that failed, we then reduced the IXIA’s send rate until all packets were transmitted.

For testing, we identified five interesting categories of Ourmon filters and constructed filter tests for these categories.

null: The packet capture filter is on by default and is

the only filter used. hard: The hardwired C filters as a group. bpf: BPF filters as one or more filter-sets. top-n: The top-N flow filter mechanism.

Table 2: Maximum Packet Tests

test BPF top-n BPF drop
sets flows min (KB/s) rate
null filter  128  0%
hardwired  128  0%
top-n  1000 128  0%
top-n  10000 XXX  80%
BPF  1  128  0%
BPF  4  128  0%
BPF  8  128  20%
BPF  8  7168  0%
test config 1  1000 7168  0%

combo: A simple combination of all filters.

The null filter tells us whether or not the BPF in the kernel was losing packets, as its count/drop information is taken from the operating system. The hard, bpf, and top-N filter categories were tested individually in order to determine if the filter type itself had an impact on the overall performance. The six hardwired C filters available at the time of testing were used in the tests. The bpf tests were based on a filter-set that had 4 simple expressions in it. The individual BPF expressions were configured to capture TCP ports that could not match the output of the IXIA (UDP packets). It seemed reasonable for BPF expressions to always fail to match.

Repeatedly testing the top-N filter with the same IP flow would yield no new information. Therefore, for the top-N test we used a rolling IP destination setup where each subsequent UDP packet within a set of 1000 or 10000 had a different IP destination. This could be said to be a rough simulation of the Slammer worm, with its variation in IP destinations.

4 Test Results

Test results fall into two basic categories, which are reported separately: tests with maximum-sized packets, and tests with minimum-sized packets.

4.1 Maximum Packets

In this set of tests, packets were always 1518 bytes, the normal maximum MTU for Ethernet packets. (This works out to a 986 Mb/s flow of UDP packets). Tests included the null, hardwired, top-N (with different destination flow counts), bpf, and combo tests.

The test results are summarized in Table 2. The flow rate was set to maximum. The drop rate

therefore shows packets lost at GigE speeds. In the null case, the configuration almost worked with the typical BSD default BPF buffer size of 4 KB/s. However, some packets were lost at a 30 second interval. This may have had something to do with an operating system timer. In-Table 3: Minimum Packets and Null Filter

BPF buff (KB/s) drop thresh (Mb/s)
32  53.33
128  68.52
256  76.19
512  76.19

creasing the kernel BPF buffer size to 128 KB/s resulted in perfect transmission, even after adding in the hardwired filters.

The top-N flow filter worked with no loss at 1000 flows and completely failed at 10000 flows. Larger BPF buffers did not help (shown as XXX in the table). This is the most significant failure case with maximum-sized packets. Decreasing the IXIA flow rate to 45 Mb/s resulted in perfect transmission. For the bpf tests, we increased the number of filters to 8 sets (32 BPF expressions) before running into some loss. At that point, we increased the kernel BPF buffer size. We found that a very large buffer of 7 MB/s could indeed get us back to lossless transmission. With the combo configuration (hard + top-n + 1 bpf set) we did not experience any loss. Note however that we used only 1000 flows with the top-N filter.

4.2 Minimum Packets

Attempts to capture maximum-rate flows of minimumsized packets (64 bytes) uncovered serious problems. We therefore report our results as a series of small experiments. Each experiment focuses on a different test domain.

4.2.1 Null Filter Only

With the null filter, we are not doing any significant application work. Consequently, this test determines whether the kernel driver and buffer subsystem plus the application read can actually capture packets. It was not always possible to capture all packets even in the null filter case. Instead we attempted to determine the effect of the kernel BPF buffer size on drop rates as shown in table 3.

A buffer size of 256 KB/s appears optimal. At this size the system begins to drop packets at 76 Mb/s. Larger kernel buffers do not improve the result. Of course the most important aspect of this test is that we cannot capture more than around 10% of the GigE stream without loss. (Note that packet overhead for minimum packets results in a maximum data flow of around 760 Mb/s.)

Table 4: Hardwired and BPF Tests

test  BPF sets flow (Mb/s) drops
hardwired  76  0%
BPF  1  68  0%
BPF  2  53  0%

Table 5: Minimum Packets—top-N Tests

flows drops buffer (KB/s) flow (Mb/s)
1  0%  256  76
100  1%  256  76
1000 25% 256  76
1000 0% 256 45
10000 50% *  *

4.2.2 Individual Filter Types

Having determined baseline drop rates using the null filter, we could now proceed to measure the impact of other filter types. In the bpf filter-set tests, we tried both one and two filter-set configurations. In the top-N filter test, we varied the number of simultaneous flows. Table 4 shows the results for the hard and bpf tests. Table 5 shows the results for the top-N tests.

Hardwired filters appear to have no impact on performance. The bpf filters have some performance impact, visible even at a modest 76 Mb/s transfer rate. At this transfer rate, 1000 unique flows is stressful for the top-N filter. However reducing the flow rate to 45 Mb/s allows the filter to keep up with the data. 10,000 unique flows cannot be handled with any kernel buffer size at any measured transfer rate.

4.2.3 Combination filtering

In this experiment we measure the combo filtering previously discussed. Here we vary only the flow rate, holding the buffer size constant at 256 KB/s and the number of flows constant at 1000. Table 6 shows the results.

We see that we must reduce the flow rate to roughly one-half maximum in order to prevent drops. This is probably because of the impact of 1000 flows on the top-N filter. The filters here are in truth fairly minimal, as there is only one BPF filter-set. In reality one would want more filter-sets to get better traffic information. The bottom line is that we must reduce the flow rate to 38 Mb/s for even a modest amount of work to be performed without packet loss. Not only are small packets hard to deal with even for bare-bones applications that do no real work in processing them, but real levels of work will likely reduce the amount of processing power to very small throughput rates.

Table 6: Minimum Packets—All Filter Types

flow (Mb/s) drops
76  44%
68  37%
53  18%
45  03%
38  0%

5 Mitigation

The poor performance of the Ourmon probe on even modest flows of small packets was of obvious concern. In the real world in PSU’s DMZ we feel that Ourmon is useful as an anomaly detector even under conditions of severe packet loss. Indeed, such a loss constitutes an anomaly indicator in its own right. Nonetheless, the exhibited performance of Ourmon in the lab on minimumsized packets was unexpectedly poor. This poor performance was a threat to some of the conclusions reached in real-world use. Several strategies were thus pursued in improving probe efficiency.

The top-N flow filter has been both one of Ourmon’s most useful tools and one of its least performant. It was observed that the hashing/caching strategies and data structures used in the initial implementation of this feature could be vastly improved. In optimizing the flow filter code, our main strategy was aimed at improving the runtime hashing mechanism. A simple but key improvement was in choosing an appropriate size for the hash buffer. The hash function and hashing strategy were also changed to directly address the problem of hashing a traditional flow tuple. Other efficiency improvements included inlining the basic search and insert functions and the hash function itself.

The user-level interpreted BPF filter performance was also a cause for some concern. Our real-world PSU DMZ probe has recently been seeing peaks around 40000 pkt/s, and has had 80 BPF expressions in its configuration. The top-N flow filter used to be the main bottleneck. Over a number of years we have increased the number of BPF expressions used in our DMZ and have made the BPF sub-system the new contender for that honor. Our idea for improving packet filtering performance was not a particularly clever one, but was quite effective.

We have created a simple runtime facility, CBPF, for hand-coding commonly used BPF expressions as C subroutines. The probe configuration file may refer both to BPF expressions and to CBPF functions. One may thus optionally replace a commonly-used interpreted BPF expression with a call to a C function via a function jump table. For example, roughly half of the BPF expressions we are using in our DMZ simply watch subnets.

Consider this sample BPF subnet-recognizing expression from a configuration file:

bpf "subnets" "subnet 1"
"net 192.168.1.0/24"

The CBPF replacement for this is simple:

cbpf "net" "subnets" "subnet 1"
"192.168.1.0/24"

This is not terribly sophisticated engineering, but it gets the job done. The BPF interpreter remains useful, as its expression language is versatile. In general, however, long or commonly-used BPF expressions can be optimized; most of the filtering in the PSU Ourmon configuration is now being performed by CBPF.

Ourmon has been modified in order to analyze the effect of our optimizations. Instead of taking data from the network, we can capture real packets in the PSU DMZ with tcpdump. We can then take the dump data and feed it to the Ourmon probe. This allows us to use gprof profiling to determine relative speed improvements for code changes.

We recently conducted an experiment using 10 million packets— roughly 1 GB of data—from our DMZ. We compared the difference between a not-yet-released version of Ourmon and the older Ourmon version 2.2. Ourmon 2.2 lacked top-N optimizations and CBPF support. (The currently released version 2.4 of Ourmon has the top-N optimizations in it. However, while CBPF will be available in the next release, it is not in 2.4.) We compared the performance of CBPF expressions versus BPF for filtering; we also analyzed the performance of old versus new top-N code. The performance improvements were gratifying. CBPF expressions proved to be roughly 10 times faster, with the performance improvement depending on the complexity of the expression. The improved top-N facility was roughly 30 times faster.

As expected, these mitigations resulted in greatly improved performance when fielded in the PSU DMZ. As with most large intranets, the traffic volume in the PSU DMZ is quite cyclic. When Ourmon used interpreted BPF expressions for all filtering, it routinely dropped many packets during peak usage times. Installing Ourmon with CBPF essentially eliminated this problem. Figure 8 illustrates this phenomenon. The upper line in the figure indicates weekly traffic volume. The lower line indicates the number of dropped packets during the time period when Ourmon CBPF was introduced. The date the optimization is installed (week 5) is quite apparent from the graph.

Other mitigations are highly desirable, but require much more effort. Such schemes might include: placing the probe in the kernel; various forms of small-scale threaded parallelism done either at the application or kernel level on a SMP platform; and putting the probe into a network card, possibly using something like the Intel IXP [2] network processor which uses embedded parallel processors. We have experimented to some extent with all three of these options. In the near term we intend to pursue the more portable application-level parallelism option, although we do not rule out an IXP port.

6 Related Work

Ourmon touches on a number of areas of computing research and practice. Minimally, any discussion of related work must include comparison with existing tools, background related to the BPF, and work on the problem of processing small packets.

In a broad sense Ourmon could be compared to simple sniffers like tcpdump. Ourmon’s goal is somewhat different, however. Sniffers focus on displaying a serial list of individual packet contents based on one expression. Ourmon, on the other hand, tries to summarize many packets and give a parallel view of network traffic via many concurrent BPF expressions.

The measurement system closest to Ourmon is probably Ntop [5]. Ntop and Ourmon are both open source. Ntop is a program that can be said to be vertically integrated. It combines the probe and graphics engine functionality of Ourmon plus a web server into one program. Ntop could be said to have a desktop orientation. It derived its name from its origins as a network version of the UNIX top program. Ourmon was designed more along the lines of the traditional distributed SNMP RMON probe from which it derives its name. Ourmon easily decomposes into a two-CPU system design separating capture and display. Ourmon also has a network-statistical feature set more in keeping with recent cultural trends in network engineering. For example, Ourmon relies heavily on RRDTOOL-based graphics. RRDTOOL is also used by other popular network management tools like Cricket [21, 20]. Ntop has a much better graphical user interface than Ourmon— perhaps there may be room there for future joint effort. Close examination of the feature sets of Ourmon and Ntop shows significant differences.

Ourmon might also be compared to closed-source commercial tools like SNMP RMON II. One of the original design goals of Ourmon was to provide a rough open source equivalent of SNMP RMON. This notion includes the fundamental probe versus graphics engine two-CPU design. On the other hand, Ourmon deliberately used a human-debuggable TCP-based ASCII tuple format in an effort to avoid the complexity inherent in the implementation and use of the ASN.1 remote procedure call notation. Ourmon makes no attempt to use a

Figure 8: Packet losses without/with CBPF

standardized network protocol to join the probe and the graphics engine. On one hand, this inhibits interoperability of these components. On the other hand, it allows easy and unconstrained changes in component communication with each new version.

In a similar vein, Ourmon could also be compared to Cisco’s NetFlow[1]. We should point out that there exist some open source NetFlow probes. However typically one encounters a NetFlow probe in a more expensive commercial router or switch. At least one open source NetFlow collector, NEye [13], is available; the reporting functionality of NEye does not yet appear to be comparable with that of the closed-source Cisco collector. NetFlow aggregates IP flows along the traditional lines of an IP flow tuple: IP src, IP dst, protocol, L4 src, L4 dst ports. Ourmon’s new tuples, such as its TCP SYN tuple, tend to be targeted toward specific features interesting for anomaly detection. Commonly, these are statistics about a single IP source. Our not-yet-released experimental version of Ourmon includes statistical analysis based on Layer 7 payloads. This sort of analysis is currently impossible to perform using NetFlow, and it is difficult to see how small changes to the NetFlow architecture could accomodate it.

From the intrusion detection point of view, Ourmon and Ntop are somewhat similar. They are lightweight tools that show anomalous behavior via graphs. In contrast, an Intrusion Detection System (IDS) tool like Snort does signature-based analysis on every packet. One could argue that Ourmon is lightweight compared to Snort. Ourmon looks principally at the layer 1–4 network headers and almost entirely ignores the data payload. It is thus reasonable to expect that Snort’s processing will be impacted even more than Ourmon’s by flows consisting of high volumes of small packets. As Ourmon’s analysis of data payloads increases, this difference may decrease. On the other hand, future versions of Snort might also be expected to increase their level of analysis. The bottom-line difference is simply that Ourmon is an anomaly detector and analysis tool, while Snort is primarily a signature-based IDS.

Other researchers have studied the problem of capturing high-volume flows of small packets. For example, Mogul and Ramakrishnan [11] describe the phenomenon of receive livelock, in which the network device driver bottom-half runs to the exclusion of higherlevel code that processes received packets. They present improved operating system scheduling algorithms that can lead to fair event scheduling, with the result that receive interrupts cannot freeze out all other operating system events.

One must consider that there is not a lot of time to process packets. A maximal packet flow of about 1.5 million small packets per second works out to approximately 700 nanoseconds per packet! Some sophisticated approach, such as improving the individual compute performance of various filter mechanisms or applying parallelism, is needed to attain adequate performance. A recent IDS [8] contains an interesting parallel hardware engine based on a flow slicing technique. This hardware reportedly improves Snort’s performance under high packet loads. However constructing such a system in such a way that it effectively uses parallelism and yet remains cost-effective is a challenge.

Recently, researchers have been working on enhancements to the BPF with the goal of improving BPF performance. For example, the xPF system [6] expands the BPF to a general purpose computing machine by allowing backward branches. This provides the opportunity to enhance BPF performance by running filters entirely in-kernel. The BPF+ system [3] optimizes BPF performance using both machine-code compilation and various optimization techniques. Our CBPF, while much less sophisticated, echoes the goals and general method of this latter work.

7 Analysis

Our experimental work leads to some interesting recommendations and observations:

1. The default FreeBSD BPF buffer size of a few KB/s was chosen for tcpdump on older, slower networks: this size is inadequate for more thoroughly monitoring a modern network. We suggest that modern UNIX/Linux kernels adopt a larger default buffer of at least 256 KB/s. This size should not unduly burden modern systems, and should improve the performance of most network monitoring tools. Network administrators should understand that a multimegabyte buffer on the order of 8 MB/s may be needed for a full network monitoring system such as Ourmon. Larger buffers should improve the performance of the Linux packet socket, should minimize the loss of large packets by network sniffing applications such as Snort.

2. Our BPF filters seem to have a kernel buffer cost associated with them. Our results suggest that there is a relationship between the amount of kernel buffer space needed to mask filter latency and the number of BPFs used in our application. Our tests seem to imply that the BPF mechanism is less costly than the top-N filter. However the BPF mechanism can have any number of expressions, and the expressions themselves can vary in complexity. It is thus hard to compare the BPF filter mechanism to the top-N filter mechanism in terms of compute power.

3. The real computation problem for the top-N system is that it is driven to extremis under attack attempting to cope with random IP source and/or destination IP addresses. The hash-based top-N algorithm will first search for the given flow ID, and then perform an insert if it fails to find the flow. Consequently random flows always cause an insert. This leads to an interesting research question: How can we deal with boundary conditions caused by random IP addresses without unduly impacting efficiency mechanisms meant for normal bursty flows?

4. Our 2 GHz Pentium-4 class computer cannot capture more than 10% of the minimum-sized packet flow. Worse, if the computer is expected to perform actual application-level work using the data, the fraction of packets we capture without loss falls below 5%. Small packets mean big trouble.

This last item deserves extended discussion. Consider an IDS system such as Snort. A signature-based IDS system wants to run an arbitrary number of signatures over both the packet headers and the packet data, and may choose to store its measurement results in a database. Clearly per-packet processing times become quite large in this scenario.

Now consider the security principle known as weakest link. For example, Bruce Schneier writes [18]: “Security is a chain. It’s only as secure as the weakest link.” An IDS system incurs a significant risk when it drops a single packet. The dropped packet may be the one with the Slammer worm that will infect an internal host. Worse, a set of coordinated systems might launch a distributed DOS attack against an IDS monitor, first blinding it with small packets and then sneaking a one-packet worm payload past it. Packet capture for small packets at high rates is an important open security problem.

It should also be pointed out that the natural evolution of any network monitoring tool is toward more functionality at the expense of more work. For example, we have recently added many new kinds of list tuples to Ourmon in addition to our original top-N flow tuple. Many of these new tuples are useful for both network management and anomaly detection, We are also starting to expand our packet analysis work to Layer 7. In the extreme case, Snort and Ourmon could be combined.

In addition use of the Internet is always growing. When Ourmon was originally placed a few years ago in the PSU DMZ, we saw 20 K pkt/s at peak times. Now we see peaks of 40 K pkt/s. When both new tool features and packet counts grow, and these factors are combined with the possibility of large distributed attacks, it is fair to say that the computation overhead problem is nontrivial.

A related open research question: other than by trial and error, how does one determine if a measuring system is powerful enough to fit the needs of a certain network configuration? From real-world experience in our DMZ, we know that a 3 GHz P4 is challenged by 40 K pkt/s flowing from a moderately-loaded 100 Mb/s network connection. Such a system, however, may work well with a 10 Mb/s Internet connection. An administrator faced with 500 Mb/s peaks, or even an OC-3 (155 Mb/s) faces a difficult problem in specifying a hardware environment adequate for use with a signature-based IDS such as Snort, a network monitor and anomaly detector such as Ourmon, or even a firewall.

Our future work will be aimed in several directions. We think that the small packet problem must be addressed. We plan to further investigate various parallel architecture notions. In particular, we are hoping that a threaded SMP solution will prove sufficient. This could lead to an open source multi-platform (BSD/Linux) probe.

Although we have not provided many details of our recent anomaly detection work in this paper, we believe that our recent work in the last year in that area has been promising. (See section 8 below for more technical information.) We intend to steer Ourmon further in this direction. For example, we are beginning to investigate lightweight Layer 7 payload scanning statistics that may help us find peer-to-peer applications as well as logical networks composed of IRC bots (“botnets”). We are also studying the effects of various statistical schemes for cheap classification of attackers and peer-to-peer applications.

We are in the process of bringing up an automated trigger-based packet capture facility. This facility allows us to specify a threshold for any BPF expression and for some of the top-N style graphs. Packets are captured during peak times in which a threshold is exceeded, and stored in tcpdump capture files for later analysis. This facility should prove useful in characterizing and analyzing anomalies reported by Ourmon.

8 Conclusion

Ourmon is a novel tool for network monitoring aimed principally at anomaly detection and analysis. Experiments measuring the performance of both the underlying kernel BPF filter system and the Ourmon frontend filter systems have led to dramatic improvements in Ourmon’s performance. Ourmon’s probe uses the BPF and its CBPF in a flexible fashion, allowing the user to group a small set of related filter expressions into a single RRDTOOL graph. Ourmon also provides various graphs and reports about tuple lists keyed by flow IDs, IP source addresses, and L4 ports, which are intended to summarize statistically significant network events.

Ourmon fills an important niche in the open source network monitoring toolset. It also points up some fundamental performance issues in network monitoring. Addressing these issues in Ourmon has led, and should continue to lead, to improvements in general networking monitoring performance and a better understanding of these performance issues.

Availability

Ourmon is freely available at http://ourmon.cat. pdx.edu/ourmon under the BSD License. More technical information may be found for the cur-

                                              rent release at http://ourmon.cat.pdx.edu/
                                              ourmon/info.html.

9 Acknowledgements

This work would not have been possible without the generous donation from the IXIA Corporation of an IXIA 1600 tester. We also wish to thank our graduate students Subha Singaram and Gayathri Nagabhushan for their help with the measurement project.

References

[1] Cisco Systems. Cisco CNS NetFlow Collection

                                                  Engine. http://www.cisco.com/en/US/
                                                  products/sw/netmgtsw/ps1964/
                                                  products_user_guide_
                                                  chapter09186a00801ed569.html, April

2004.

[2] M. Adiletta, M. Rosenbluth, D. Bernstein, G. Worich, and H. Wilkinson. The Next Generation of Intel IXP Network Processors. Intel Technology Journal, August 2002.

[3] A. Begel, S. McCanne, S. Graham. BPF+:

Exploiting Global Data-flow Optimization in a Generalized Packet Filter Architecture. Proceedings of ACM SIGCOMM. September 1999.

[4] CERT Advisory CA-2003-04 MS-SQL Server

                                                  Worm. http://www.cert.org/
                                                  advisories/CA-2003-04.html,

November 2003.

[5] L. Deri and S. Suin. Practical Network Security: Experiences with ntop, IEEE Communications Magazine, May 2000.

[6] S. Ioannidis, K. Anagnostakis, J. Ioannidis, and A. D. Keromytis. xPF: Packet Filtering for Low-Cost Network Monitoring. In Proceedings of the IEEE Workshop on High-Performance Switching and Routing (MPSR), May 2002.

[7] Karlin, Scott, Peterson, Larry, Maximum Packet Rates for Full-Duplex Ethernet, Technical Report TR-645-02, Department of Computer Science, Princeton University, Feb. 2002.

[8] C. Kruegel, F. Valeur, G Vignka, R. Kemmerer. Stateful Intrusion Detection in High-Speed Networks. In Proceedings IEEE Symposium Security and Privacy, IEEE Computer Society Press, Calif. 2002.

[9] Leffler, et. al., The Design and Implementation of the 4.3BSD Unix Operating System, Addison-Wesley, 1989 [10] S. McCanne and V. Jacobson. The BSD Packet

Filter: A New Architecture for User-level Packet Capture. In Proceedings of the Winter 1993 USENIX Conference, San Diego, January 1993.

[11] J.C. Mogul and K.K. Ramakrishnan. Eliminating Receive Livelock in an Interrupt-Driven Kernel. In ACM Transactions on Computer Systems, 15(3):217-252, August 1997.

[12] D. Moore, V. Paxson, S. Savage, C. Shannon, S. Staniford, N. Weaver. The Spread of the Sapphire/Slammer Worm. http://www.cs.

berkeley.edu/~nweaver/sapphire.

2003.

[13] NEye, an Open Source Netflow Collector.

http://neye.unsupported.info.

February 2005.

[14] Ntop distribution page. http:

//sourceforge.net/projects/ntop.

March 2004.

[15] Ourmon web page.

http://ourmon.cat.pdx.edu/ourmon,

March 2004.

[16] M. Roesch. Snort—Lightweight Intrusion Detection for Networks. In Proceedings of the USENIX LISA ’99 Conference, November 1999.

[17] RRDTOOL web page. http://people.ee.

ethz.ch/~oetiker/webtools/rrdtool.

March 2004.

[18] B. Schneier. Secrets and Lies. p. xii. Wiley Computer Publishing. 2000.

[19] Tcpdump/libpcap home page.

http://www.tcpdump.org, March 2004.

[20] Cricket home page.

http://cricket.sourceforge.net,

March 2004.

[21] J. Allen. Driving by the Rear-View Mirror: Managing a Network with Cricket. In Proceedings of the USENIX First Conference on Network Administration ’99 Conference, April 1999.

[22] Waldbusser, S. Remote Network Monitoring Management Information Base Version 2. IETF. RFC 2021, January 1997.