Enhancements to Statistical Protocol IDentification (SPID) for self-organised QoS in LANs

Conference Paper (PDF Available) · August 2010with147 Reads
DOI: 10.1109/ICCCN.2010.5560139 · Source: DBLP
Conference: Proceedings of the 19th International Conference on Computer Communications and Networks, IEEE ICCCN 2010, Zürich, Switzerland, August 2-5, 2010
Since most real-time audio and video applications lack of QoS support, QoS demand of such IP data streams shall be detected and applied automatically. To support QoS in LANs, especially in home environments, a system was developed, which enables self-organised QoS for unmanaged networks through host implementations in contrast to traditional solutions without network support. It supports per-link reservation and prioritisation and works without a need for application support. One part of this system is an automated traffic identification and classification system, which is subject of this paper. An efficient set of attribute meters, based on the Statistical Protocol IDentification (SPID), was investigated, enhanced and evaluated. We improved the performance, added support for UDP protocols and real-time identification. It is shown that using our implementation efficient near real-time protocol identification on per-flow basis is possible to support self-organised resource reservation.
Enhancements to Statistical Protocol IDentification (SPID)
for Self-Organised QoS in LANs
Christopher K
, Christian
, Florian Adamsky
, Veselin Rako
, Muttukrishnan Rajarajan
, Rudolf J
School of Engineering and Mathematical Sciences
City University London
Northampton Square, London EC1V 0HB, UK
{Christopher.Koehnen.1, Christian.Ueberall.1, V.Rakocevic, R.Muttukrishnan}@city.ac.uk
Department for Information Technology, Electrical Engineering & Mechatronics
University of Applied Sciences FH Giessen-Friedberg
Wilhelm-Leuschner-Str. 13, D-61169 Friedberg Germany
{Florian.Adamsky, Rudolf.Jaeger}@iem.fh-friedberg.de
Abstract—Since most real-time audio and video applications
lack of QoS support, QoS demand of such IP data streams
shall be detected and applied automatically. To support QoS
in LANs, especially in home environments, a system was
developed which enables self-organised QoS for unmanaged
networks through host implementations - in contrast to tradi-
tional solutions, without network support. It supports per-link
reservation and prioritisation and works without a need for
application support. One part of this system is an automated
traffic identification and classification system, which is subject
of this paper. An efficient set of attribute meters, based on
the Statistical Protocol IDentification (SPID), was investigated,
enhanced and evaluated. We improved the performance, added
support for UDP protocols and real-time identification. It was
shown that using our implementation efficient near real-time
protocol identification on per-flow basis is possible to support
self-organised resource reservation.
Keywords-Classification; Statistical Analysis; SPID; Packet
Identification; QoS;
Today, multimedia services increase dramatically in home
and private networks [1]. Television services over the net-
work (IPTV) and voice over ip (VOIP) services have a
demand for high bandwidth capacity and very strong quality
of service (QoS) needs [2], [3]. To guarantee these, common
QoS strategies like IntServ, using RSVP, or DiffServ have
to be supported by the network. Since in most home or
private networks low cost hardware is used, a support
of these techniques cannot be assumed. Beside this, web
video applications tunnel their streams using HTTP and are
therefore not easily distinguishable from common Internet
traffic. This results in less technology acceptance by users,
since lacking QoS leads to a lower quality of experience
(QoE) level [4]. Especially for IPTV providers the network
plane inside the households is unpredictable, as providers
only can influence the QoS level until the transfer point
to the house. In addition to that is the network a shared
medium, in contrast to the traditional TV cable, which leads
to new challenges for a traditional service to achieve the
accustomed QoE level. One approach to increase QoE in
LANs is the QoSiLAN system [5]. It is based on several
core technologies and works in three phases. In a first phase
physical network discovery algorithms and QoS parameter
tests run through, to generate a detailed map about the local
network and its available resources. For this purpose, the
Mircosoft LLTD protocol [6] was reimplemented extended
to efficiently make use of its topology and QoS analysis
functionality. In a second phase traffic monitoring, analysis
and policing is performed. The research on this part is the
subject of this paper. Finally network resources are reserved
and prioritised for the monitored flow. Here signalling
based on the NSIS protocol’s NSLP for QoS Signalling [7]
framework is applied to coordinate QoS issues between the
network hosts.
In contrast to common QoS strategies QoSiLAN doesn’t
depend on router or switch support to enable QoS, but
makes use of it, if available. The central entity, which
performed the mapping and monitoring advises all hosts in
the network to shape and DSCP-mark their traffic according
to its policies and advises. But only those traffic flows need
to be shaped, whose data-paths affect physical links where
current reservations apply.
A. Port-Based Identification
The TCP/UDP-port-based packet identification is the sim-
plest method to classify traffic. By using this method the port
numbers of the packets are inspected and mapped to the
IANAs list of well-known ports. Moore et al. [8] showed
that approximately only 70% of the traffic can be identified
correctly that way. One of the problems, when using port-
based packet identification, is that many applications like
P2P may not have a registered port, which makes it com-
pletely impossible to detect them. Additionally some ports
are ambiguous. One example is port 888, which is used
by the CD Database Protocol (CCDP), the AccessBuilder
[9] and the RAID controller 3DM and 3DM2 protocols.
Another problem with port-based classifiers is the increasing
number of applications that use the HTTP’s port 80 to bypass
firewalls. Also security attacks are not associated with an
specific port and can therefore not be detected using port
mappings. Overall is an identification based on the port
numbers not a reliable method anymore.
B. Deep-Packet-Inspection
The Deep Packet Inspection (DPI) is more reliable tech-
nique to inspect packets on application level, which is the
layer 7 in the OSI reference model. In layer 7, a packet can
be scanned for application specific signatures. One disadvan-
tage of this approach is the human intervention to analyse
the application and to create an unique application signature
[10]. This analysis entails a lot of effort, even if a RFC or
whitepaper exists. If no application documentation exists, it
is very difficult to reverse-engineer the targeted application’s
protocol. Another drawback emerges, when the application
protocol doesn’t carry application significant values. In this
case there is no chance to identify the application. DPI
also causes significant privacy issues, since it also scans the
payload of plain text protocols like e.g. SMTP, HTTP, POP3,
etc., which may carry unencrypted private data. This may
also lead to false positive results.
C. Machine-Learning Techniques
Machine-learning algorithms are powerful for traffic clas-
sification as shown in [11] and can be categorised as
supervised, unsupervised and semi-supervised learning [5].
Supervised learning means that labelled training data are
required to classify flows. In contrast, unsupervised learning
algorithms work with unlabeled data. They don’t classify,
but cluster the flow into groups. The third variant, the
semi-supervised machine learning algorithms makes use of
both techniques. It needs a mixture of a small amount of
labelled training data and a large amount of unlabelled
training data for identification and learning. Many machine
learning algorithms need full-flow statistics, which makes it
difficult to identify flows in real-time [10]. Therefore these
approaches are appropriate to us, because our goal is to
detect a particular flow and classify it in near real-time.
Since most of these algorithms have a high complexity,
the computing costs are too high for embedded real-time
A. Algorithm Details
The SPID algorithm was deployed by Erik Hjelmvik
and Wolfgang John and is a statistical approach to identify
applications and protocols [12]. There is no need to search
for unique application signatures and in addition real-time
detection after the 10th packet is possible. Both features
define the advantages of this kind of technique. The SPID
algorithm works in three steps as presented in figure 1. At
first all packets must be grouped together as bi-directional
flows. In the case of TCP a flow starts with a three-way-
handshake. A flow will be identified by source IP and port,
destination IP and port and transport protocol (5-Tupel).
But only packets carrying a payload are significant. This
means that e.g. all TCP-ACK packets are not relevant for
the statistics.
Figure 1. The SPID algorithm works in three phases.
When a flow reaches a minimum number of 10 packets it
becomes subject of the actual analysis. Plain-text protocols
can be identified after the 10th packet, but binary or HTTP-
tunnelled protocols need up to 20 packets for reliable
detection as pointed out in section IV. c. By making use
of several statistical methods, samples are taken from each
flow, e.g. byte frequency, entropy, direction changes and
many more. After this step, it branches into identification or
approximation. Approximation is the learning part, in which
new protocols can be included or old ones improved. The
learning part works by making use of the empirical law of
large numbers. The identification is the heart of the SPID
algorithm. It compares the probabilities of the trained flows
with the observed flows by calculating the Kullback-Leibler
divergence [13], as shown in equation 1. It is the fingerprint
that is compared for protocol identification
𝐷(𝑃 ∣∣𝑄) = 𝐾𝐿(𝑃, 𝑄) =
𝑃 (𝑥) log
𝑃 (𝑥)
the Kullback-Leibler divergence is a logarithmic measure
of the relation between the relative frequency of the ob-
served (𝑃 ) to the trained flows (𝑄), summed for each
attribute measure. The result is then compared to the list of
known protocols. The protocol with the nearest divergence
(𝐷(𝑃 ∣∣𝑄)) is then identified. The distance represents the
probability. The threshold to identify a flow as unknown
was experimentally determined, as presented in figure 4.
B. Implementation
We reimplemented the SPID algorithm with some differ-
ences to Hjelmvik to gain the performance and to support a
wider range of protocols, as explained in the following. To
ensure a general compatibility even on small scale embedded
hardware, we implemented it using the C++ programming
language and the libPcap library for portability. New at-
tribute meters were implemented to support UDP and near
real-time identification. We have optimised the fingerprint
database to keep as small as possible and flexibly exchange-
able. In our case the fingerprint database with 17 protocols
has a size of 389KB, whereas Hjelmvik’s consumes 9.8MB
for 12 protocols, using a XML-format. This was achieved
by choosing a binary format and defining a variable array
size for each attribute meter.
Our focus was on streaming protocols like RTP, MPEG-
TS, MMS or RTMP as well as on progressive downloads like
flash videos and WMV/OGG streams which are tunnelled
through HTTP. We chose 12 protocol attribute meters, which
actually worked out very well and with good performance
for our purposes.
C. Protocol Attribute Meters
The protocol attribute meters are the statistical measure-
ments of this approach.
byte-frequency: This measurement operates on the first
TCP packet of each direction and counts the frequency of
a byte in the payload [12]. Encrypted or compressed data
appear in an even distribution, whereas data in plain-text
show an uneven distribution. See figure 2 for an example.
byte-frequency of the first 32 bytes: Most UDP pro-
tocols contain little clear-text information and have only
a small header, therefore the byte-frequency of the whole
payload results in a high divergence value. To avoid this,
we took only the first 32 bytes of the first packet and count
the frequency.
direction-changes: It measures how often the protocol
or the application communication changes the direction.
Interactive protocols like Telnet, SSH or FTP often have
direction changes, whereas streaming protocols don’t.
direction bytes meter: Percentage of amount of data,
which was sent from the client to the server and vice versa.
Through this measurement a distinction is possible between:
upload and download balanced (e.g. RTP for VoIP, ...)
almost only download (e.g. HTTP, POP3, ...)
almost only upload (e.g. SMTP, IRC, ...)
0 50 100 150 200 250
2 · 10
4 · 10
6 · 10
8 · 10
= ’e’
= ’i’
Byte in decimal
Relative Frequency
Figure 2. Byte Frequency Histogram
entropy: The entropy is a measurement for the amount
of random information within a system [14]. The maximal
entropy of 256 possible bytes is 𝐻(𝐼)
= 8. It is applied
to the first packet in each direction. E.g. plain-text protocols
with natural language in it, have low entropy values, while
those with encrypted or compressed data have high ones.
first 4 bytes hash-function: The first four bytes of the
first packets in each direction are very characteristic for
most protocols [15]. For the first four bytes a hash-value
is calculated. To simplify matters we make use of a cross-
sum. An sample hash function for HTTP is shown in table
1: 47 45 54 20 G E T / = 166
2: 48 54 54 20 H T T P = 206
Table I
action reaction first 3 byte hash meter: It generates a
hash-function of the first 3 bytes of each packet that wasn’t
sent in the same direction as the previous one. The idea
behind this measurement is to get a connection between
a request and a response, especially for command based
protocols like HTTP, FTP or POP3.
byte pairs reoccurring: This measurement identifies
bytes that occur more than once within the first 16 bytes
of the first packet. E.g. it identifies the “SS” in the SSH-
banner or the “TT” in the HTTP GET and POST request.
unicode frequency: It scans for alphabetical unicode
strings in the first five packets and saves the byte frequency
for it. Since some protocols like WMV streams or MMS
[16] use unicode strings in their protocols.
first 8 bytes equality meter: It checks how often the
first 8 bytes are equal, since this is significant for protocols
that have a fixed header in every packet, e.g. the RTP [17]
or MPEG-TS [18].
first bit positions meter: Especially UDP protocols have
a small header, which consists of fields and flags on bit
level. This fact makes it hard for the byte frequency meter
to detect the particular protocol. This attribute meter counts
the frequency of a single bit in connection with its offset.
The first 128 bits will be viewed and counted.
first payload size: Payload length of the first packet
in a flow [19]. In most cases the first packet contains
information for initialisation of a session. The first
packet size of a HTTP session is only between 120 and
1000 bytes. POP3 for example is between 10 and 100 bytes.
Implementation experiences revealed, that some attribute
meters are not very accurate and adulterated the results. For
this reason the duration of flows, port numbers, packet size
and the inter-arrival-times were not included for the SPID
calculation. Instead, we included additional attribute meters
to enhance the results. These were the number of direction
changes, the first payload size, the entropy and the unicode
A. Datasets and Implementation Setup
An important part of the research presented here was the
creation and procuration of datasets. On one hand we created
datasets under laboratory conditions, and on the other hand
we used freely available datasets
and extracted the
specific protocols. On creation, we paid extra attention to
capture training data from application with as different
versions and implementations as possible. E.g. for HTTP
we used various browsers on different operating systems to
get a wider spectrum of payload data. In order to do this, we
automated as much as possible using Perl scripts. To capture
network traffic we used the packet analysers Tcpdump
. Overall we gathered 3135 pure flows taking
more than 1.5GB for the 17 protocols we tested.
B. Evaluation Methods
Evaluations are performed by the formula for the Recall,
the Precision and the F-Measure to analyse the accuracy and
stability of this approach [20], as presented in equations 2
to 4. It is necessary to know the true-positives (TP), which
represent all the flows that are identified correctly. The false-
positives (FP), represent the not or wrong identified flows.
The false-negatives (FN), represent the other protocols that
were wrongly identified. The F-Measure is the weighted
combination between recall and precision.
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇 𝑃 + 𝐹 𝑁
𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇 𝑃 + 𝐹 𝑃
𝐹 𝑀 𝑒𝑎𝑠𝑢𝑟𝑒 =
2 𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑅𝑒𝑐𝑎𝑙𝑙
𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
C. Results
The figure 3 contains the final results created using 30
trained session each.
Precision F-Measure
Figure 3. Protocol Identification Results
For a more detailed view, see table II, which presents the
underlying data.
Protocol # Val. Rec. Prec. F-Meas.
BT 30 197 76.65% 98.05% 86.04%
FLV 30 70 98.57% 90.70% 94.52%
FTP 30 461 97.62% 100.00% 99.80%
HTTP 30 127 91.34% 88.55% 89.92%
IRC 30 100 95.00% 100.00% 98.51%
MMS 30 252 94.05% 100.00% 96.93%
MPEG-TS 30 40 100.00% 100.00% 100.00%
OGG 30 122 97.06% 100.00% 98.51%
POP3 30 55 100.00% 96.49% 98.21%
RTMP 30 112 99.11% 100.00% 99.55%
RTP/UDP 30 69 100.00% 100.00% 100.00%
SMTP 30 464 100.00% 100.00% 100.00%
SSH 30 136 98.53% 100.00% 99.26%
SSL 30 41 100.00% 100.00% 100.00%
Telnet 30 812 99.01% 100.00% 99.50%
TFTP 30 42 97.62% 100.00% 98.80%
WMV 30 35 100.00% 68.63% 81.40%
Table II
It’s shown that this implementation achieves good results
for most of all the tested protocols. The HTTP identification
is less good, since it is a more loosely described protocol
[12], e.g. it is difficult to differentiate between a flash
video and a website, which is compressed with gzip or
which loads lots of images using a single TCP connection.
Also a lower precision was archived for WMV streams,
since most WMV data is not only progressively tunnelled
through HTTP, but also streamed using the MMS protocol.
That’s why they are often mixed up by the classificator.
The BitTorrent protocol makes use of the Message Stream
Encryption (MSE) [21], to complicate identification through
several obfuscation techniques and encryption. The Accu-
mulatedDirectionBytesMeter [15] is a special attribute meter
to identify BitTorrent. We do not make use of this meter,
since it blurs significantly the results for the other protocols.
Figure 4 shows the F-Measure depending on the number of
trained flows.
10 20 30 40 50
Number of Trained Flows
F-Measure in %
Flash Videos
OGG Streams
WMV Streams
Figure 4. The F-Measure Depending on the Number of Trained Flows
There one can see that with the number of trained flows
the stability of the identification becomes better. The graph
shows that after the 20th packet the F-Measure for all
protocols reaches the 80 percent marker, which is a critical
value for a stable protocol identification. For plain-text
protocols like IRC, Telnet, SMTP and so on, is a stable
identification possible after the 10th packet.
Figure 5 shows the F-Measure with 30 trained flows
depending on the number of inspected packets with payload.
As shown in this figure, the F-Measure has risen rapidly
after the 13th packet for OGG vorbis and WMV streams.
Only after this point there is a distinction possible between
those two. The F-Measure evens out after the 20th packet.
This is early enough to initiate actions, e.g. to prioritise the
particular flow. For other protocols like Telnet, SSH or TFTP
a stable identification after the 10th packet is possible. Figure
6 shows the F-Measure for 30 trained flows and 20 inspected
packets depending on the threshold.
This threshold value is compared with the sum of the
values of the attribute meters. If it is equal or lower, the
5 10 15 20 25 30
Number of Inspected Flows
F-Measure in %
Flash Videos
OGG Streams
WMV Streams
Figure 5. F-Measure Depending on the Number of Inspected Packets
protocol is known. If it is higher than the threshold, the flow
is marked as unknown. It also helps to avoid the detection
of false-positively identified flows. The optimal value was
experimentally determined as 13, using the results presented
in figure 6. At this point an optimal identification with
a good precision is possible and unknown protocols are
correctly identified as unknown.
2 4 6 8 10 12 14
F-Measure in %
Flash Videos
OGG Streams
WMV Streams
Figure 6. F-Measure Depending on the Threshold
D. Classification
The currently implemented classificator only distinguishes
between media and best-effort traffic. Media traffic is marked
for expedited forwarding, whereas best-effort traffic is not
marked. This allows for prioritisation of real-time and pro-
gressive download flows.
In order to enable self-organised, application independent
QoS in LANs, we presented an optimised implementation of
the SPID algorithm with a particular number of indicators
that allow for identification of protocols and applications
in near real-time. We archived very good results for plain-
text protocols like Telnet, SMTP POP3, IRC, etc., but
even if the payload is not human-readable good results
have been archived. Another advantage of our approach is
the flexibility of the level of granularity. For our purpose
it was important to look deeper into the HTTP to also
identify protocols which are tunnelled, like progressive video
downloads. To include new protocols for identification it is
sufficient to add at least 30 data set samples. This is surely
easier than looking for an unique application signature. The
math behind this algorithm is trivial, thereby a real-time
recognition even on low cost hardware is possible.
In contrast to Hjelmvik we included more protocols and
focused on media protocols, which are worthy to be priori-
tised. These were tested for their precision and robustness.
We took particular care of a small and fast implementation
and made a selection of different attribute meters for better
performance and more accurate identification. In addition,
we added UDP support to the near real-time identification
and showed the feasibility. We also showed that SPID is a
appropriate approach for identification of real-time protocols
for self organised QoS configuration. Future work will focus
on automated bandwidth estimation and resource reservation
for identified media flows
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    • "Furthermore, note that the encrypted payload of B A and B V packets generated using MSE have a high entropy and are therefore hard to detect and to block with Stateful Packet Inspection (SPI) or DPI firewalls. Statistical measurements, however, show good results to detect MSE [22, 25], but are not widespread used. Hence, use of MSE also helps in making the attack difficult to detect and circumvent, hence contributing to the evadability of the attack. "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we demonstrate that the BitTorrent protocol family is vulnerable to distributed reflective denial-of-service (DRDoS) attacks. Specifically, we show that an attacker can exploit BitTorrent protocols (Mi-cro Transport Protocol (uTP) [32], Distributed Hash Table (DHT) [30], Message Stream Encryption (MSE) [8]) and BitTorrent Sync (BTSync) [6] to reflect and amplify traffic from peers. We validate the efficiency, robustness and evadability of the exposed BitTorrent vulnerabilities in a P2P lab testbed. We further substantiate the lab results by crawling more than 2.1 million IP addresses over Mainline DHT (MLDHT) and analyzing more than 10,000 BitTorrent handshakes. Our experiments reveal that an attacker is able to exploit BitTorrent peers to amplify the traffic up to a factor of 50 times and in case of BTSync up to 120 times. Additionally, we observe that the most popular BitTorrent clients are the most vulnerable ones.
    Full-text · Conference Paper · Aug 2015
    • "The classification feature is realised on basis of an efficient implementation [9] of the Statistical Protocol IDentification (SPID) algorithm [10]. It's a statistical approach to identify network protocols and works in three steps: At first all packets must be grouped together to bi-directional flows according to their 5-tupel. "
    Full-text · Dataset · Jun 2014
    • "While machine learning algorithms using flow features for traffic classification received substantial attention, the content-based approaches mainly relied a simple pattern matching [13]. In recent studies, however, several hybrid solutions based on machine learning methods and taking into account content features were proposed [22, 23, 18, 24, 25, 12]. In this section, we present a brief survey of some popular methods (cf.Figure 3.4) used in classification approaches discussed in the previous section. "
    [Show abstract] [Hide abstract] ABSTRACT: The subject of traffic classification is of great importance for effective networkplanning, policy-based traffic management, application prioritization, and securitycontrol. Although it has received substantial attention in the research communitythere are still many unresolved issues, for example how to classify encrypted trafficflows. This thesis is composed of four parts. The first part presents some theoreticalaspects related to traffic classification and intrusion detection, while in the followingthree parts we tackle specific classification problems and propose accurate solutions.In the second part, we propose an accurate sampling scheme for detecting SYNflooding attacks as well as TCP portscan activity. The scheme examines TCPsegments to find at least one of multiple ACK segments coming from the server.The method is simple and scalable, because it achieves a good detection with aFalse Positive Rate close to zero even for very low sampling rates. Our trace-basedsimulations show that the effectiveness of the proposed scheme only relies on thesampling rate regardless of the sampling method.In the third part, we consider the problem of detecting Skype traffic and classi-fying Skype service flows such as voice calls, skypeOut, video conferences, chat, fileupload and download. We propose a classification method for Skype encrypted traf-fic based on the Statistical Protocol IDentification (SPID) that analyzes statisticalvalues of some traffic attributes. We have evaluated our method on a representativedataset to show excellent performance in terms of Precision and Recall.The last part defines a framework based on two complementary methods for clas-sifying application flows encrypted with TLS/SSL. The first one models TLS/SSLsession states as a first-order homogeneous Markov chain. The parameters of theMarkov models for each considered application differ a lot, which is the basis foraccurate discrimination between applications. The second classifier considers thedeviation between the timestamp in the TLS/SSL Server Hello message and thepacket arrival time. It improves the accuracy of application classification and al-lows efficient identification of Skype flows. We combine the methods using a NaiveBayes Classifier (NBC).We validate the framework with experiments on three recentdatasets—we apply our methods to the classification of seven popular applicationsthat use TLS/SSL for security. The results show a very good performance.
    Article · Nov 2012
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