approach, Allen investigated web application attacks
in IPv6 traffic, but no IPv6 specific attacks. All three
IDS systems were tested with the same network cap-
tures of a web application vulnerability scan: one over
IPv4, and one over IPv6. While the three IDS systems
behaved different in their number of alerts, each IDS
reported a comparable amount of alerts for the IPv4
and IPv6 traffic as one would expect.
Salih et al. present a tool which detects and clas-
sifies covered channels in IPv6 networks (Salih et al.,
2015). Sources of covert channels are header fields
which are not set properly. Salih et al. identify
the following header fields as potential covert chan-
nels: Traffic Class, Flow Label, Payload Length, Next
Header, Hop Limit, and Source Address. They pro-
pose a Machine Learning approach to detect covert
channel implementing an enhanced feature selection
algorithm supporting Naive Bayesian classifier. The
results of the conducted experiments show a high de-
tection performance of about 96 %. While this is a
promising result, it is questionable whether these at-
tacks should be detected by an intrusion detection sys-
tem, since the misuse of IPv6 header fields may al-
ready be prohibited by a packet filter.
In order to prove that IPv6 attacks are real and
exploitable, Marc Heuse implemented different link-
local attacks in “The Hacker’s Choice” (THC) toolkit
(Heuse, nd). The toolkit implements several denial-
of-service and fragmentation attacks as well as a
covert channel attack where a destination option
header is misused. The toolkit is easy to use also for
non-security experts, constantly updated and runs on
Linux. We use the THC toolkit as the base of our
IDSv6 benchmark (see Section 3).
Fernando Gont developed the SI6 Networks’ IPv6
toolkit
1
. Like the THC toolkit, the SI6 implements
link-local attacks special to IPv6, but it is highly con-
figurable and may be used for protocol analysis as
well as attack purposes. The tools need to be param-
eterized correctly in order to be effective and usually
require more protocol knowledge and configuration
overhead than the THC toolkit. Contrary to the THC
toolkit the SI6 implementation runs also on BSD-type
UNIXes such as FreeBSD and MacOS.
3 THE IDSV6 BENCHMARK
One question that is going to be asked by researchers
and practitioners alike is: ”How effective is a partic-
ular IDS in detecting network attacks”? It is possi-
ble to use the THC toolkit and SI6 directly to mea-
1
https://www.si6networks.com/tools/ipv6toolkit/
sure the effectiveness of attack detection and defense
mechanisms but this method has several drawbacks.
Software versions change also for attack tools and
may implement subtile changes that can lead to vary-
ing detection results. Necessary network setups de-
pend on additional devices and their configuration, are
time consuming to create and may be difficult to repli-
cate exactly. Attributing changes in detection results
to specific root causes becomes difficult, even if the
same tools are used for different measurements.
The problem of comparatively benchmarking dif-
ferent Intrusion Detection Systems is not new. Hav-
ing a common benchmark dataset eases comparison
between different systems and makes trial runs easily
reproducible. Between 1998 and 2000, Lincoln Lab-
oratory of MIT released several datasets for the eval-
uation of IDS, the so called DARPA datasets
2
. The
datasets were aimed to represent real-world traffic
mixes interspaced with attack traffic. Right after re-
lease, these datasets drew immediate criticism regard-
ing the modelling and generation of the attack and
background traffic (McHugh, 2000), however, they
are still used to the present day, simply because no
other common benchmarks exist.
When we started developing the IPv6 Plugin Suite
for Snort, we were looking for a quick way to ver-
ify our development efforts. In order to automatize
attack detection testing and to minimize administra-
tive overhead, we created the IDSv6 benchmark suite,
that now consists of several pcap files derived from
real attack tools created in the network setup depicted
by Figure 1. This collection of pcap files can be ap-
plied quickly and consistently in order to test detec-
tion rates and functionality of intrusion detection sys-
tems. The IDS present in the figure plays no active
part during capturing of the attack traffic. It simply is
a placeholder to indicate from what position within
the network traffic patterns will be observed if the
benchmark pcap files are used. IDS usually support
direct input from capture files. If this is not possi-
ble, tools like tcpreplay
3
could be used to reproduce
previously captured network events.
Table 1 lists all the different attacks that are
included in the suite. With the exception of
dos mld chiron, all attacks were created by the THC
toolkit in Version v3.5-dev. We choose the THC
toolkit because of its maturity and ease of use, which
makes it likely to be used during real network attacks.
The Chiron tool can be found on Github
4
. We used
Version 1.0 of this tool.
The IDSv6 benchmark is focused on attacks that
2
https://www.ll.mit.edu/r-d/datasets
3
https://tcpreplay.appneta.com
4
https://github.com/aatlasis/Chiron
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