A honeypot-driven cyber incident monitor:
lessons learned and steps ahead
*, Shankar Karuppayah
§, Panayotis Kikiras*, Max Mühlhäuser
Telecooperation Group, §National Advanced IPv6 Center, *AGT International,
TU Darmstadt - CASED Universiti Sains Malaysia Darmstadt, Germany
ﬁrst.firstname.lastname@example.org email@example.com firstname.lastname@example.org
In recent years, the amount and the sophistication of cyber
attacks has increased signiﬁcantly. This creates a plethora
of challenges from a security perspective. First, for the eﬃ-
cient monitoring of a network, the generated alerts need to
be presented and summarized in a meaningful manner. Sec-
ond, additional analytics are required to identify sophisti-
cated and correlated attacks. In particular, the detection of
correlated attacks requires collaboration between diﬀerent
monitoring points. Cyber incident monitors are platforms
utilized for supporting the tasks of network administrators
and provide an initial step towards coping with the afore-
In this paper, we present our cyber incident monitor TraC-
INg.TraCI N g obtains alert data from honeypot sensors dis-
tributed across all over the world. The main contribution of
this paper is a thoughtful discussion of the lessons learned,
both from a design rational perspective as well as from the
analysis of data gathered during a ﬁve month deployment
period. Furthermore, we show that even with a relatively
small number of deployed sensors, it is possible to detect
correlated attacks that target multiple sensors.
Categories and Subject Descriptors
H.4 [Information Systems Applications]: Miscellaneous;
C.2.0 [Computer-Communication Networks]: General:
Security and Protection
Incident Monitor; Honeypot; Alert Correlation; Cyber Se-
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With the increase of cyber attacks in both numbers, as
well as in sophistication, it is becoming evident that more
advanced techniques and mechanisms are required to de-
tect attacks and to identify new trends and patterns in the
adversaries’ strategies. This also implies that relying only
upon traditional lines of defense, such as Intrusion Detec-
tion Systems (IDSs) and dynamic ﬁrewalls alone, would not
be able to provide a holistic coverage on detecting novel and
emerging patterns of attacks .
probed, attacked or compromised . They can comple-
ment other detection mechanisms, e.g., IDSs, by providing
a more active and in-depth view on attackers’ activities. At
the same time, by deﬁnition, honeypots produce zero false
positives and they are able to detect even unknown and
novel attacks. They can be classiﬁed based on the level
of interaction that is oﬀered to attackers. On the one hand,
high-interaction honeypots are real systems that exhibit cer-
tain vulnerabilities. Thus, they require constant monitoring
and their maintenance is very demanding. On the other
hand, low-interaction honeypots, simulate network opera-
tions on the TCP/IP stack level, and therefore can provide
a lightweight and straightforward defense mechanism.
Feeding honeypot alert data into a cyber-incident moni-
tor provides a number of signiﬁcant beneﬁts. As mentioned
above, in contrast to IDSs, honeypots do not produce false
positives. Therefore, the security administrator can focus
on real attacks rather than speculating from the observed
results. In addition, observing epidemic behavior, e.g., mal-
ware spreading, becomes more likely when analyzing attacks
from several collaborating honeypots.
In essence, the inclusion of diﬀerent type of alerts, e.g.,
from IDSs and honeypots, increases the aﬄuence of new
data to be processed by the administrators. However, due to
the diﬀerent focus and content of alerts generated by diﬀer-
ent systems, integrating these alerts into a single platform,
creates a multitude of challenges. Cyber incident monitor-
ing systems are developed to overcome this problem by pro-
viding an aggregation and visualization interface to unify
the various alert generators into a single system that assists
the user to make informed decisions. Starting from a broad
overview, users can decide to dive into speciﬁc alerts to as-
sess a particular reported cyber incident.
Not much work has been done that speciﬁcally focuses on
the design of a cyber incident monitor, or the lessons learned
from its usage. Dshield1, and honeymap2are two examples
of monitors that publish their data openly through the In-
ternet. In , Katti et al. provide a wide-scale analysis
focusing on correlated attacks. Even though their work can
be considered slightly outdated, it is the basis on which we
establish the deﬁnitions of correlated attacks (cf. Section
3.3). Some work has also been conducted towards provid-
ing an analysis of the lessons learned from such distributed
monitoring deployments [9, 11] and honeypots . However,
most of this work focuses only on presenting basic attack
statistics and does not consider correlated attacks.
In this paper, we present TraCINg3, which stands for TU
Darmstadt Cyber Incident moNitor, and is an open-source
centralized cyber incident monitor that supports a number
of diﬀerent types of honeypots. Tra CINg is able to visual-
ize attacks, provide statistics, present the geo-location infor-
mation of malicious users, and perform several other user-
centric operations. We explain the design rational of our
system and discuss the initial results from a ﬁve month de-
ployment period. Our results, indicate a multitude of inter-
esting ﬁndings. First, we notice many attack trends such as
the most popular protocols and ports, the most persistent
countries of origin, etc. Second, we observe that even with
a relatively small deployment of sensors, it is still possible
to detect attacks manifested by the same source on multiple
sensors. Furthermore, these correlated attacks motivate the
need for collaboration between diﬀerent monitoring points
in detecting targeted or distributed attacks.
The remainder of this paper is organized as follows: In
Section 2, we describe our system’s design and provide jus-
tiﬁcation behind the various design choices that we made.
Section 3, provides insights and an analysis of our ﬁndings
for a deployment period of ﬁve months. Section 4, discusses
the lessons learned from our experience with the cyber in-
cident monitor, problems that we faced, as well as hints on
how to overcome them. Finally, Section 5 concludes this
2. SYSTEM DESIGN: TRACING
In this section, we provide a description of TraC IN g ’s de-
sign, including three main parts: the architecture, the dif-
ferent types of supported honeypot sensors, and the alert
2.1 Architecture and Design
TraCI N g is an open-source centralized cyber incident mon-
itor that obtains alert data from geographically distributed
honeypot sensors. In our system, sensors are currently de-
ployed in three diﬀerent continents, namely: Europe, Asia
and the America. Tra CINg follows a classic client-server
architecture and uses an HTTPS server for receiving data,
along with a Public Key Infrastructure (PKI) for the authen-
tication of the sensors. The front-end, written in node.js,
supports the integration of, e.g., OpenStreetMap  for the
visualization of attack origins and targets (cf. Figure 1(b))
and VirusTotal4for obtaining additional information on iden-
All gathered information is available to security adminis-
trators via a user-centric GUI with a plethora of diﬀerent
views (two of them shown in Figure 1) and functions, e.g.,
the creation of statistics, on-demand detailed information,
live and replay mode of the alert data, etc.
(a) Default TraCINg view
(b) OpenStreetMap integration
Figure 1: TraCINg GUI examples
TraCI N g supports and utilizes a number of sensors that
can be distinguished into two main types: dionaea honey-
pots and mobile honeypots. Here, we shortly discuss these
types of honeypots along with the implementation speciﬁcs
of our sensors.
Our main detection mechanism uses the dionaea5low-
interaction honeypot, that is considered to be the state of
the art in its honeypot class, i.e., low-interaction . We
utilized dionaea honeypots in two diﬀerent ways. First,
we deployed dionaea on regular machines, i.e., virtual ma-
chines (VMs), as well as Raspberry Pis. The latter allows
for an easy copying of systems, to be able to reuse them, and
introduces convenient plug-n-play support for the sensors.
Second, we also deployed some dionaea sensors as cloud
instances. This is important for being able to monitor attack
traﬃc that may be speciﬁc to geographical regions. Thus,
via the utilization of cloud providers, we were able to deploy
our sensors in diﬀerent continents. Furthermore, cloud ser-
vices provide resilience and uptime reliability for our sensors
which ensures uninterrupted monitoring.
TraCI N g also provides support for obtaining data from
mobile honeypots, i.e., from HosTaGe which is developed
by us in our former work [19, 18]. HosTaGe is a lightweight
low-interaction honeypot for mobile devices for detecting
malicious wireless network environments. The motivational
idea behind HosTaGe is to alert users on the security status
of the wireless network they are connected to. In addition,
the combination of HosTaGe’s collaboration techniques and
synchronization with TraC I Ng, creates additional beneﬁts
for the users. For instance, users can learn from others,
about the global security status of wireless networks in their
city, or country. Moreover, the diverse data that is sent to
TraCI N g can also provide us with valuable and more accu-
rate input for the detection of correlated attacks and latest
trends from the behavior of malicious users and malware.
TraCI N g supports input from any type of honeypot or
IDS, with the only restriction being to utilize the respec-
tive input interface for submitted alerts. Sensors need to
convert their alerts into a well-determined JSON6format
that is being supported by the input interface. Therefore,
it is straightforward to add support for other honeypots.
For instance, to enable TraC INg support for the dionaea
honeypot, we implemented the respective alert export func-
tionality as an internal dionaea module.
Another important property, that is related to alerts, is
the frequency of the exchanged alerts. In the current version
of our system, alert data is sent instantaneously upon the
detection of an attack by the honeypot to TraC I Ng,except
for HosTaGe honeypots in which, the exchange frequency is
determined by the user or by the auto-upload functionality
(when the mobile device has Internet connectivity, e.g., via
a wireless network). Take note that the frequency of the ex-
changed alerts could also lead to some attacks on the sensors
(cf. Section 4 — Probe-Response attacks).
The generated alerts that are submitted to TraCI N g con-
tain the following attributes:
•Time-stamp: The date and time speciﬁcs of an attack.
•Id: A unique identiﬁer for each alert.
•Sensor Type: A ﬁeld that diﬀerentiates between dif-
ferent honeypots, e.g., dionaea, HosTaGe, etc.
•IP: The source and destination IP address of attacks
(this information is excluded from the end-users’ per-
spective, i.e., from the GUI, to maintain privacy).
•Ports: The source and destination port of attacks.
•Attack type: The type of the detected attack, e.g., a
portscan, a shellcode injection, etc.
•Geo-location Information: The geo-IP information, and
the name of the city and country.
•Authorization Status: A boolean id that indicates whether
a sensor posses a signed certiﬁcate from the main TraC-
INg Certiﬁcate Authority (CA) via the usage of a PKI.
•MD5 hash: The MD5 hash of the malware collected
from a honeypot (when applicable).
•Log : Whenever possible, the whole communication be-
tween the attacker and the sensor is logged, and can
be presented to the user on demand.
3. ALERT DATA ANALYSIS
In this section, we present the results of a ﬁve month ob-
servation period (March to July 2014) of TraC I Ng with a
total of ﬁve sensors deployed in three diﬀerent continents.
3.1 System Setup
During the period of our analysis, we collected data from
five diﬀerent honeypots that were continuously monitoring
and sending alerts to TraCI N g. The speciﬁcs of our deployed
sensors are summarized in Table 1. In more details, two
sensors were located in Malaysia within a /24 sub-network.
In addition, two sensors were deployed as cloud instances
in USA within diﬀerent /8 networks. Finally, one sensor
was deployed in Greece. As we will also discuss in Section
3.3, the dynamics of the alert data are important for the
subsequent analysis of the attacks, especially in a correlated
Sensor Name Country Common Subnet Preﬁx
MY-01 Malaysia /24
USA-01 USA /8
GR Greece -
Table 1: Sensor description
For our analysis, we restrict our evaluation to the station-
ary deployed dionaea honeypots only. Data gathered from
our mobile honeypots was removed as the number of users
making use of the mobile honeypot, at that time, was im-
mature (and restricted only to Germany). Thus this would
introduce bias into our analysis. Nevertheless, our analysis
shows that even with a relatively low number of sensors, a
large amount of distributed and correlated attacks can be
A preliminary overview of recent results gathered from us-
age of mobile honeypots, between March and May of 2015,
is as following. Approximately 1000 alerts were sent from
four diﬀerent countries (i.e., Germany, United Arab Emi-
rates, Italy, and Columbia). In a glance, 62% of the attacks
were shellcode injection, 24% were targeting the TELNET
protocol, 6.6% were targeting HTTP, and ﬁnally 4% were
MySQL brute-force attacks.
3.2 Data analysis
In total, our sensors recorded 898,570 alerts during the
examined period, from 30,101 distinct IP addresses, hav-
ing their origin in 146 diﬀerent countries. However, it is
interesting to note that attacks from USA and China alone,
represented about 80% of the total number of alerts recorded
by our system. Nevertheless, we have veriﬁed that this ob-
servation is not inﬂuenced by the geographical location of
the deployed sensors.
Table 2, presents a summary of the most popular attack
types along with the number of attack occurrences. The
attack description column of the table depicts the speciﬁcs
of the attacks. In this case MySQL,MS SQL,VOIP,and
SMB refer to malicious activity speciﬁc to these protocols,
Figure 2: Graph representation of the alert data: attackers clustered close to their main targets and single-
dimensional correlation seen as edges connecting to neighbor clusters
Attack Description Total Number of Attacks
Transport Layer 116,414
VoI P 84,641
MS SQL 2,325
Shellcode Injection 420
Table 2: Most popular attack types and the corre-
sponding number of occurrences
Port Protocol/Service Number
135 RPC 24,667
139 NetBIOS 20,249
23 Tel n e t 11,058
80 HTTP 10,735
445 SMB 9,294
443 HTTPS 3,400
25 SMTP 2,558
21 FTP 1,658
110 POP3 1,153
143 IMAP 597
Table 3: Top 10 attacked ports and protocols
e.g., a brute-force attack. Moreover, the Transport Layer
describes cases in which the adversary connected to a port,
but no further activity was possible due to honeypot-related
limitations, e.g., dionaea not being able to handle the con-
nection. With respect to dionaea’s VOIP emulation capa-
bilities , the majority of the detections were brute-force
attacks and scans conducted with tools such as SipCli7.
In addition, Table 3 shows the most targeted ports and
protocols. A number of ﬁndings come as a result of our anal-
ysis. First, around 56% of the total attacks were portscans.
This can be considered as something that is expected, es-
pecially with the rise of open source Internet-wide network
scanners, e.g., ZMap . In addition, it should be noted
that bias can be introduced from such tools when utilized
by researchers. Nevertheless, we consider this insigniﬁcant
with regards to the overall analysis. Second, most of the de-
tected MySQL attacks were brute-force attacks using default
user-names and passwords. The most prominent one being
a combination of root as a user-name along with a blank
password. Moreover, Table 3 indicates that several attacks
targeting Windows OS speciﬁc protocols and services, e.g.,
the MS-RPC,andNetBIOS, are still prominent. This also
conﬁrms that several old worm variants, e.g., Conﬁcker ,
still hold an imposing position in the overall attack prop-
agation scene. Lastly, we identiﬁed that a number of the
attacks that were targeting Telne t were conducted by inse-
cure/infected embedded devices, e.g., IP web-cams.
3.3 Correlation of attacks
We classify correlated attacks in a twofold manner, diﬀer-
entiating between single-dimensional and a two-dimensional
correlation of attacks, by following a similarity-based strat-
egy . Single-dimensional correlation, groups attacks with
the same origin, e.g., the same source IP address. Thus, it
can be deﬁned as the set of alerts originating from the same
source IP address that target more than one sensor through-
out our observation, i.e., the entire observed duration of ﬁve
months. Two-dimensional correlation includes time as an
additional parameter, i.e., attacks that are observed within
a speciﬁc time window.
7SipCli VoIP audit tool: http://www.kaplansoft.com/SipCli
The analyzed dataset can be modeled as a directed graph
G=(V,E), where the set of nodes Vrepresent all IP ad-
dresses involved in the detection process, i.e., both sensors
and malicious users. The origin and the target nodes of an
attack, are represented as a set of directed edges (u, v)with
u, v ∈V, that exist between the nodes.
Figure 2, is a representation of our alert dataset, that de-
picts two major ﬁndings: attack origins can be clustered,
by vicinity, into three clusters and the single-dimensional
correlation can be seen as the edges that connect to neigh-
boring clusters. Speciﬁcally, the dataset was transformed to
a directed graph with 30,106 nodes representing all distinct
IP addresses (both sensors and malicious users), while edges
correspond to the respective connections, i.e., adversaries
connecting to the sensors. Figure 2 depicts, in a glance, an
overview of the activities in our dataset.
The ﬁve diﬀerent sensors (diﬀerentiated by distinct col-
ors) converge into the three main clusters. The clustering
is done based on the geo-location information of the sensors
(cf. Section 3.1). As such, from the five deployed sen-
sors, four of them were coupled into clusters of two. This
is also explained in our system setup description in Section
3.1. The Malaysian sensors (within a /24 network) create
tightly coupled clusters due to the high percentage of com-
mon attackers. In addition, we observe that the two sensors
in the USA, even though having distinct /8 networks, are
also close to each other. Figure 2, also clusters the attackers
based on the intensity of the alerts/attacks observed origi-
nating from them, i.e., nodes are placed closer to the sensors
they attacked intensively. Moreover, single-dimensional cor-
relation is observed when edges of a cluster (same color), are
connecting to neighboring clustering groups, i.e., an adver-
sary targeting multiple sensors.
A similar analysis, from another perspective, on the ratio
of single-dimensional correlation is shown in Figure 3. In
more details, we plot the relationship between the percent-
age of unique attackers and the targeted sensors. Almost
50% of the total number of attacks target at least two dif-
ferent sensors, during the five month period under inves-
tigation. However, a portion of this ﬁnding might be due
to the ’closeness’ of two sensors, i.e., sensors located within
the same /24 sub-network. Nevertheless, as we discuss in
the following section, even with the inclusion of time as a
parameter, we observe an almost continuous attacking be-
havior on multiple sensors simultaneously.
In single-dimensional correlation, the basis for our anal-
ysis is exclusively based on the source IP address of the
adversaries. While this is reasonable, we argue that the
time frame in which attacks take place is also an impor-
tant factor that needs to be taken into account. There is
a signiﬁcant diﬀerence between distributed attacks target-
ing multiple widespread sensors within a small time window
and the ones that target multiple sensors across longer time
period, i.e., days or even weeks.
Figure 4 presents the number of unique attackers target-
ing multiple sensors within a short time frame. In more
details, this two-dimensional correlation, is measured with
a sliding window of one hour (measurements taken every 30
minutes). The 30 minutes time interval takes into account
the state of the art in IPv4 network scanning. As of now
Figure 3: Ratio of unique attackers targeting multi-
Figure 4: Unique attackers within a sliding window
of one hour and with measurements taken every 30
one of the fastest Internet-wide scanners is a able to scan
the IPv4 address space in approximately 45 minutes (via a
random cyclic selection of IP addresses) . Thus, with our
chosen time interval we attempt to reduce bias introduced
by researchers’ activities utilizing such mechanisms.
We note that for the sake of clarity, we applied vertical
jitter to the points in the plot. Moreover, we also restricted
the ﬁgure to only two months (May to July 2014) of the
overall period. Nevertheless, similar activity was observed
for the removed time-frame. Throughout the observations,
we notice a pattern of attacking behavior in which, at least
one unique attacker is targeting three or four diﬀerent sen-
sors (within a 30 minutes observation window). Further-
more, there are also cases in which, up to four diﬀerent
adversaries targeted three sensors. Cases like this, might
indicate the discovery of a new vulnerability and thus the
possibility of extensive scans over the Internet for exploitable
insecure systems. Finally, the majority of two-dimensional
correlation was portscans. In addition, for the cases of three
and five overlapping sensors, we also detected MySQL and
VOIP brute-force attacks.
4. LESSONS LEARNED & FUTURE WORK
In this section, we discuss the lessons learned and the
shortcomings that we encountered during the design, as well
as the deployment phase of our cyber incident monitor.
Correlated attacks and number of sensors.
Although our system is already able to detect correlated
attacks, we believe that its accuracy can be further im-
proved. When sensors are sparsely deployed in the IP space,
we may miss out correlated attacks simply because the time
taken to observe such an attack is too long, e.g., more than
30 minutes. Therefore, the accuracy and eﬃciency of our
system, i.e., the required time to detect correlated attacks,
can be improved by increasing the number of sensors. This
ensures that epidemic worm propagation and new vulner-
abilities can be detected at a faster rate than the current
implementation. Thus, we plan to deploy more sensors and
distribute them uniformly across the various classes of IP
address, i.e., /8 and /16 sub-networks. Moreover, the idea
behind collecting alerts from mobile honeypots (cf. Section
2.2) also further bridges this gap of being able to detect cor-
related attacks more accurately and in a shorter time frame.
While performing stress tests to our system with a high
load of incoming traﬃc, e.g., more than 50Kof alerts per
day, we observed a bottleneck that relied in the utilized
database (which was not always able to handle these re-
quests). Thus, our initial decision to use a SQLite database
during the proof of concept phase was no longer relevant
with an increased number of alerts or sensors. We migrated
our alerts and re-deployed on MongoDB that is now able to
cater our current scenarios better and also facilitate future
An interesting real world example of such an attack peak
was during the ﬁrst quarter of April 2014. During that
timespan our cyber incident monitor had been receiving al-
most 40,000 alerts per day, with alerts being either portscans
or targeting the transport layer. We assume that this was
not a coincidence but rather related with the well known
heartbleed OpenSSL vulnerability , that was discovered
during the aforementioned days.
Although our system design provides real-time visualiza-
tion of the alerts, which is contributed by its centralized
architecture, it also presents itself as a single point of fail-
ure. Moreover, the architecture itself will turn into a bot-
tleneck when the number of new sensors is massively in-
creased, hence aﬀecting the scalability of the system itself.
We envision a new version of our system that utilizes a com-
pletely decentralized architecture and will be able to include
additional new sensors while providing increased resilience,
redundancy and scalability to the overall system.
Recently research has been conducted [13, 14, 3] that fo-
cuses on methods that can be utilized by attackers to suc-
cessfully detect monitoring sensors that publish their ﬁnd-
ings publicly via the Internet. In these attacks, so-called
probe-response attacks, the adversary produces alerts that
contain a unique watermark and starts disseminating them
over the IP address space that the attacker considers as pos-
sible for including sensors. The malicious user can subse-
quently compare the results produced from the cyber in-
cident monitor, for the speciﬁed time period in which the
probe-response attack is performed, and thus be able to cre-
ate a list of possible addresses of sensors via a reductio ad
Probe-response attacks have not been implemented in the
wild by malware. However, a malware with such capabilities
would be able to evade a large amount of cyber-incident sen-
sors and thus it will be more likely to spread unnoticed for a
longer time period. Moreover, as reported in , defending
against such attacks is not a trivial task. As such, han-
dling probe-response attacks will be one of the main consid-
erations of our future work. For instance, countermeasures
might include the addition of noise data to the publicly pub-
lished results, by following a uniform random distribution.
In addition, these attacks assume a public exposure of the
alert data, and thus can be avoided via an internal usage of
During our honeypot deployment we encountered vari-
ous problems that are related to the dionaea honeypot it-
self, and should be considered for such deployments. First,
nowadays many honeypots (including dionaea, nepenthes
, etc.) can be detected relatively easily with network scan-
ning tools such as nmap . Therefore, researchers should
consider changing the default parameters of such honeypots.
For instance, a honeypot with many conﬂicting ports active
(e.g., Windows and Linux OS speciﬁc ports simultaneously
active), is easier to be detected. Second, in some cases,
portscans have to be handled carefully. In more details, a
default dionaea installation will react to a portscan by acti-
vating services for each of the scanned ports. In the worst
case scenario, this may result to approximately 65,000 ser-
vices (i.e., all the possible port numbers) activated for each
scanned IP address. Thus, techniques to prevent such cases
should be taken into account (e.g., dionaea does oﬀer solu-
tions for this problem).
Privacy and Trust.
As part of our future goal to enrich our system with a
more diverse selection of sensors, we need to ensure that the
privacy of data from external contributors, e.g., individual
users or organizations, is assured. We plan to provide this
by including a level of ﬁltering and obfuscation that ensures
that private information, e.g., real IP addresses, are ﬁltered
and not stored in our system’s database. Inclusion of new
and unknown sensors to our system may lead to internal at-
tacks, e.g., alert spooﬁng. These attacks could in turn aﬀect
the accuracy of the entire system. Nevertheless, we plan to
overcome such attacks by deploying a trust mechanism be-
tween the participating sensors along with the existing PKI,
to ensure that the authenticity of sensors is always ensured
before accepting alerts in our system.
Distribution of Sensors.
An observation made after deploying our sensors in the
cloud was that in some cases, we received large amounts of
alerts due to attacks that seemed to be targeting particu-
larly the cloud vendor itself. More speciﬁcally, we detected
a Distributed Denial of Service (DDoS) while we were ex-
perimenting with the deployment of sensors in the same
cloud provider; yet in totally diﬀerent geographical loca-
tions. Therefore, it should be noted that although many
cloud providers oﬀer deployment of instances all over the
world, the range of IP addresses that is utilized is speciﬁc
and in many cases, well known to malicious entities. More-
over, if sensors are only placed within a single cloud provider,
this may introduce bias in the ﬁnal results; attacks that tar-
geted only the cloud provider(s) might be misinterpreted as
though they were attacks taking place all over the globe.
Sophisticated adversaries as well as the increased number
of attacks in general, create challenges both in the detec-
tion and the analysis phase. Furthermore, the successful de-
tection of correlated attacks, requires the collaboration be-
tween diﬀerent monitoring points. Cyber incident monitors
provide a way for researchers and security administrators to
eﬃciently create a holistic view of their monitored networks.
In this paper, we presented a cyber incident monitor,
TraCI N g, that is driven by honeypots. We discuss about its
architecture and the various design choices that were made.
In addition, we provide an analysis of data gathered from
TraCI N g, during a period of five months. Our alert data
analysis indicates a plethora of ﬁndings: ﬁrst, many attacks
target protocols that are considered as deprecated, e.g., Tel-
net, are still prominent, while newer protocols, e.g., VOIP,
also begin to experience increased attacks. Second, we ob-
serve large-scale portscans that, as a result of our correlation
analysis, appear to target a wide range of IP addresses.
We would like to thank ISLAB from the National Center
for Scientiﬁc Research“Demokritos” (Greece), as well as the
Malaysian Research and Education Network (MYREN) for
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