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Intrusion Detection on System Call Graphs


Abstract and Figures

Cyber attacks such as ransomware can do great damage. Intrusion detection systems can help to detect those attacks. Especially with anomaly detection methods, it is possible to detect previous unknown attacks. In this paper, we present a graph-based approach in combination with existing methods trying to increase recognition rates and reduce false alarm rates. Our Hypotheses: By taking the inherent structure of the underlying data into account, it is possible to gain more insights compared to other known methods. The modern ADFA-LD dataset was used for the evaluation, which reflects the operation in a modern operating system. Compared to the Stide approach we demonstrate that a graph-based approach can keep pace.
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Intrusion Detection on System Call Graphs
Martin Grimmer Martin Max Röhling Matthias Kricke
Bogdan Franczyk Erhard Rahm
Cyber attacks such as ransomware can do great damage. Intrusion de-
tection systems can help to detect those attacks. Especially with anomaly
detection methods, it is possible to detect previous unknown attacks.
In this paper, we present a graph-based approach in combination with
existing methods trying to increase recognition rates and reduce false
alarm rates. Our Hypotheses: By taking the inherent structure of the un-
derlying data into account, it is possible to gain more insights compared
to other known methods. The modern ADFA-LD dataset was used for
the evaluation, which reflects the operation in a modern operating sys-
tem. Compared to the Stide approach we demonstrate that a graph-based
approach can keep pace.
Intrusion Detection, Anomaly Detection, System Call Graph,
host-based IDS
1 Introduction
Nowadays all computer systems face the threat of attacks. Hardly a week
goes by without reports of new malicious software or hacking attempts. As
seen in May 2017, cyber attacks like the ransomware WannaCry may cause
massive damage to the IT infrastructure of companies and to private users
]. This shows the importance of detecting attacks on computer systems.
Host based intrusion detection systems (HIDS) are capable to achieve this.
Generally speaking, there are two types of HIDS: misuse detection and anomaly
detection systems. The misuse detection system defines abusive behavior
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and identifies processes conducting otherwise. These definitions are called
misuse signatures or attack patterns. However, one drawback of these systems
is the need of signatures before an attack occurs. Therefore, they are not
able to detect attacks not defined in the misuse signatures. In contrast to
misuse detection systems, anomaly detection systems, which are also called
behavior based detection systems, are based on the definition of ordinary system
behavior. These systems detect deviations from the ordinary behavior and
do not need any attack signatures beforehand, which allows them to detect
previously unknown attacks. Anomaly detection is related to the fields of
outlier and novelty detection. Since we want to learn normal behavior of a
system to distinguish it from an unknown, anomal and intrusive behavior we
move forward into the area of one-class classification problems.
In the case of one-class classification, only information of one class is
available. The task is to define a boundary around this class so that it accepts
as much of the containing objects while minimizing the number of accepted
outlier objects [2].
This paper aims at extending an established method with a graph based,
anomaly detecting approach to host based intrusion detection systems. After a
period of training on a set of normal sequences of system calls, our approach is
able to determine whether a new given system call trace belongs to an anomaly
or normal behavior.
The rest of this paper is organized as follows: Section 2 introduces a brief
background and lists other approaches. Section 3 explains the theory behind
our approach using system call graphs. Section 4 lists our used features and
classifiers, which form the central algorithm for anomaly detection. Section 5
presents results and evaluates our approach. Section 6 discusses our results and
provides an outlook for further research.
2 Background and Related Work
2.1 Host-Based Intrusion Detection Systems
HIDS uses system call traces to detect intrusions. The intrusions are in form of
anomalous subsequences of the traces. These collective anomalies translate to
malicious programs, unauthorized behavior and policy violations [3].
A system call trace is the ordered sequence of system calls that a process
performs during its execution, like open,read or write as shown in Figure 1 [
Grimmer et al.: Intrusion Detection on System Call Graphs G-3
Trace 1 open read mmap mmap open read mmap ...
Trace 2 open mmap read open close ...
Trace 3 open close open close open mmap close ...
Figure 1: Sample traces of operating system calls [3]
The amount of possible system calls can be described as the alphabet based on
the operating system. The size of the alphabet is usually large and depends on
the system architecture. For example, about 380 individual system calls exist in
the kernel used by the Ubuntu 11.04 distribution [
]. The traces are extracted
from the kernel of the operating system. In a linux environment, tools like
strace allow to monitor programs and to report all executed system calls. Each
program generates sequences by its own characteristics in different lengths and
different system calls [3].
It is essential that anomaly detection techniques, which are applied to HIDS,
consider the context of the data. A system call trace which is compromised
is clearly different from the program process under normal conditions [
For this reason point-anomaly detection techniques which can consider an
individual data instance as anomalous, cannot be applied to this domain [3].
2.2 Other Approaches
Stide [
] keeps track of sequences of predetermined length seen in the training
set and calls sequences not seen in training mismatches. To detect anomalies,
the number of mismatches in a region of 20 consecutive sequences is considered.
The threshold for anomalies is set between 1 and 20.
T-Stide [
] is an extension to Stide, which incorporates a threshold (for
example any sequence seen less than 0,001% of total number of sequences) for
rare sequences which are also counted as mismatch.
Eskin presents a probabilistic approach for detecting anomalies without
the need of a clean normal training data [
]. The approach is based on three
assumptions: Normal data can be modeled using the probability distribution,
anomalous elements are sufficiently different from normal elements and the
number of normal elements in the dataset is significantly larger than the number
of anomalous elements.
Creech tries to build a language which uses discontiguous system calls in
order to get a semantic context. This context was used to train the decision
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engine of a neural network, the extreme learning machine (ELM) [
]. ELM was
proposed to reduce the false alarm rate while increasing the anomaly detection
rate. Finally, superior results were demonstrated for the semantic approach. A
considerable disadvantage of this approach is the necessary processing time,
which can last weeks.
3 Our Approach: System Call Graphs
Hypotheses 1.
By taking the inherent structure of the underlying data into
account, it is possible to gain more insights compared to other known methods.
3.1 n-gram Sequence Graph
Each program trace represented by a sequence of system calls can be interpreted
as a graph. A system call of such a sequence is represented by a vertex in the
graph, the transition from a system call to the subsequent system call by an
directed edge. Repeated calls of the same system call refer to the same vertex
in the graph. The following example is intended to illustrate this. Given the
sequence Sfrom Figure 2 the resulting sequence graph looks like Figure 3.
S: 054 175 120 175 175 003 175 175 120 175 ...
Figure 2: Example system call sequence
. Note that for simplicity’s sake, the
system calls are displayed as integers, as it is done in the reference dataset we
use as shown in section 5.1.
Figure 3: Sequence Graph for the example sequence
. Edge weights represent
the frequency of the transition and are omitted if the frequency is 1.
Grimmer et al.: Intrusion Detection on System Call Graphs G-5
This principle can also be applied to n-grams of the system calls from a
sequence, as shown in Figure 4 and 5. The window defining the n-gram is
incremented by one place regardless of the n-gram length.
step 1 [ 054 175 ] 120 175 175 003 ...
step 2 054 [ 175 120 ] 175 175 003 ...
step 3 054 175 [ 120 175 ] 175 003 ...
Figure 4: Selection of the first three n-grams from
which form the vertices
(054, 175), (175, 120) and (120, 175) in the resulting n-gram sequence graph
with n=2.
Figure 5: Resulting Sequence Graphs for the example sequence
. Again, edge weights represents the frequency of the transition and
are omitted if the frequency is 1.
3.2 n-gram Probability Graph
The n-gram Probability Graph is generated from all sequence graphs, from
the training set of normal system call sequences. Vertices with the same label
are merged. Parallel edges from vertices
are grouped together and the
frequency of the corresponding transition is noted as a parameter of the edge
called transition frequency
. In addition, the probability of a transition
from a vertex
to an adjacent vertex
is stored as edge parameter and called
transition probability
. Let
be the set of adjacent successor vertices of
a, than:
p(a,b) = f(a,b)
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Figure 6 shows an example probability graph and its two source sequence
Figure 6: Example probability graph generated from the training set of two
sequence graphs with
. The edge properties are transition frequency and
transition probability.
The resulting n-gram Probability Graph is similar to a markov chain of n-th
order. For a given system call sequence
it gives the probability regarding the
training set off all transitions of
considering the
previous system calls of
the sequence.
4 Classification
4.1 Feature Engineering
We investigate the properties of the graph best suited to detect anomalies in the
system call sequences. In this paper we want to use these properties in addition
to the feature used by Stide.
Features we considered are: the number of distinct vertices used by a
sequence, the mean of transition probabilities and the mean of transition fre-
Grimmer et al.: Intrusion Detection on System Call Graphs G-7
quencies. These values can easily be calculated from a given sequence graph as
indicated in Figure 7.
example sequence transition frequency probability
ab f (a,b)p(a,b)
175 - - -
175 - - -
003 175,175 175,003 1 0.333
175 175,003 003,175 2 0.666
054 003,175 175,054 0 0.000
175 175,054 054,175 0 0.000
120 054,175 175,120 1 0.500
175 175,120 120,175 3 1.000
mean: 1.166 mean: 0.4165
Figure 7: Another example sequence applied on the probability graph of Figure
6 and the average of the corresponding transition frequencies and probabilities.
This average value is used as one feature in the following classification.
We normalized the values of all of our features with z-score normalization
using Gaussian distribution.
4.2 Classifier
We classified the extracted features of the system call traces using the following
classifiers: axis aligned bounding box,k-centers and nearest neighbor.
In the following descriptions we call the set of training sequences
T={t1,t2, ...,tnT},
the set of features:
F={f1,f2, ..., fj}with j=|F|
and we call the actual feature vector of a given sequence s:
Fs= ( f1(s),f2(s), ..., fj(s))
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4.2.1 Axis Aligned Bounding Box Classifier
The Axis Aligned Bounding Box Classifier (AABB) is the simplest classifier
we used. It defines the boundary around the target class as an
axis aligned bounding box. For each feature
used for classification it
stores the minimum
and maximum
value seen in the training set. A
given sequence
is considered an anomaly if at least one of its features
lies outside the axis aligned bounding box:
sis an anomaly ↔ ∃ fiF:fi(s)<min fifi(s)>max fi
Figure 8: The Axis Aligned Bounding Box Classifier visualized for two di-
mensions. The simple form of the border can cause problems. As one can see
normal values like
can be classified correctly, but anomalies like
may not
be detected.
4.2.2 K-centers Classifier
The k-centers method introduced by Ypma and Duin [
] covers the dataset
balls of equal radii. The centers of the balls lie on elements of the
Grimmer et al.: Intrusion Detection on System Call Graphs G-9
training set. The
centers are determined by a random initialization and a
search strategy trying to minimize the maximum minimum euclidean distance
of the selected
-centers to all other elements of the trainings set. The result
of this training are the
selected centers
C={c1,c2, ..., ck}
and the minimized
maximum minimum distance
: A given sequence
is considered an anomaly
if the distance of sto all k-centers is greater than r.
sis an anomaly ↔ ∀ cC:||Fsc||2>r
Figure 9: K-centers Classifier visualized for two dimensions and
. The sets
of training, normal and anomal data are the same as in Figure 8. The slightly
more complex form of the border may provide better classification results. This
time, the anomal data point
is classified correctly, but the normal point
4.2.3 Nearest Neighbor Classifier
The nearest neighbor method from [
] compares the euclidean distance from
a test sequence
to its nearest neighbor
within the trainings set to the
distance of nn(s)to its nearest neighbor nn(nn(s)) from the trainings set:
sis an anomaly ↔ ||Fsnn(Fs)||2>||nn(Fs)nn(nn(Fs))||2
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Figure 10: Nearest Neighbor Classifier visualized for two dimensions. As
before, the sets of training, normal and anomal data are the same as in Figure 8.
This more complex form of the border may provide even better classification
results. The example data points
are classified correctly in this example.
4.2.4 Combined Classifier
We also want to test a combined variant of the classifiers presented here. It
should decide by majority voting. The classifier contains a configuration of
each of the other classifiers and a threshold value that specifies how many of
the classifiers must classify a trace as normal in order to mark it as normal.
5 Evaluation
5.1 Datasets
In order to ensure the comparability of the results, we want to use a frequently
used dataset from recent years. Although the 1999 DARPA Intrusion Detection
Evaluation dataset [
], especially the BSM (Basic Security Module) from the
MIT Lincoln Labs and the Sequence-based Intrusion Detection dataset from
Grimmer et al.: Intrusion Detection on System Call Graphs G-11
the University of New Mexico [
], were often used in recent years, we want
to apply our approach to the newer ADFA-LD dataset [
]. This dataset
consists of sequences of system calls without parameter similar to the sequences
used in the previous sections and as shown in Figure 2. The composition and
size of the ADFA-LD dataset is shown in Figure11. Past approaches provide
dataset number of sequences
training data 833
validation data 4373
attack data 745
Figure 11: Composition of the ADFA-LD dataset.
good results on older datasets as mentioned above, but especially the new
ADFA-LD is much more difficult to solve, because it has a much greater degree
of similarity between attack data and normal data [
]. In contrast to earlier
datasets, the traces of ADFA-LD come from a mixture of different programs
without mapping the traces to the programs. Therefore, it is not possible to
learn the normal behaviour of a single program, instead one has to consider
everything as a whole. It uses a modern Linux operating system that is fully
patched as well as the latest publicly accessible exploits. In summary the ADFA-
LD record is based on a realistic modern target and is much more representative
of current cyber attacks [
]. The ADFA-LD thus forms a realistic and relevant
metric for the IDS performance evaluation.
5.2 Stide Variants
In order to compare the performance of our approach we have implemented
two variants of the Stide approach.
Variant I corresponds to the approach described in [
]. A threshold value
of 1 to 20 is defined. If the number of mismatches within a locality frame of
the 20 last n-grams is greater or equal l, the trace is considered an anomaly.
Variant II considers the whole trace at once and calculates the ratio of
mismatches to the number of n-grams of the trace. In this variant, a threshold
for the ratio is defined, which determines whether it is an anomaly.
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5.3 Results
To visualize the results of our experiments, we use receiver operating character-
istic (ROC) curves plotting the detection rate (DR) against the false alarm rate
(FAR). These values are calculated as follows:
DR =anomal traces classified as anomal
FAR =normal traces classified as anomal
N={n1,n2, ...nnN}
the set of normal traces for validation and
{a1,a2, ...anA}the set of anomal traces for validation.
The detection rate is the percentage of intrusions (anomal traces), which are
correct identified as anomal (true positives). The false alarm rate is defined to be
the percentage of normal traces which are declared anomalous (false positives).
The difference of the detection rate to 100% describes the percentage of attacks
which are not detected (false negatives).
The best results we achieved are optimal in Pareto condition, so that the
detection rate cannot be improved without increasing the false alarm rate. In this
condition the detection rate is maximized and the false alarm rate is minimized at
the same time. In order to keep up with other approaches, detection rates above
80% with as low as possible false alarm rates are relevant for us. Exceptions
to this rule are results with an extremely low false alarm rate of less than 1%,
which is necessary for real-world application.
5.3.1 Stide Variant Results
The results from comparing both variant implementations of Stide can be seen
in Figure 12.
As one can see, variant II is better than variant I in almost all respects. This
situation only changes with very high false alarm rates. Similar curves were
measured for other values of
. Therefore, in the following we refer to variant
II only.
Grimmer et al.: Intrusion Detection on System Call Graphs G-13
Detection Rate in %
False Alarm Rate in %
Stide Variant I
Figure 12: Resulting Detection Rates and False Alarm Rates for
of both
Stide variants with different values for the thresholds described in section 5.2.
Figure 13 shows more results of variant II of our implementation.
Detection Rate in %
False Alarm Rate in %
Figure 13: Results from our implementation of the Stide variant II approach
with different values for nand the threshold.
This variant of Stide is capable of achieving a DR of about 80% with a cost
of about 18% FAR or a DR of 90% with 24% FAR. The best result from our
experiments with a very low FAR of under 1% is achieved by the configuration
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and a value of
for the threshold achieves 18.9% DR and 0.7%
5.3.2 Other Classifiers on n-Gram Probability Graph Results
We did similar experiments for the classifier described in section 4.2. The
results are shown in the following Figures.
Figure 14 shows: the nearest neighbor classifier is obviously not suitable
for the given data and features and is omitted in the following considerations.
However, the AABB classifier is quite promising for its simple approach.
Detection Rate in %
False Alarm Rate in %
AABB Classifier
NN Classifier
Figure 14: ROC curves of AABB and NN classifiers for different values of n.
Figure 15 shows the ROC curves of the k-Center classifier for different
values of
. For each value of
we tested several values from 1, 26, 51,
76, ..., 501. The quality of the ROC curve depends highly from
. For low
values of
the DR is quite poor but rises up to over 90% at the expense of FAR
with over 30%.
Grimmer et al.: Intrusion Detection on System Call Graphs G-15
Detection Rate in %
False Alarm Rate in %
Figure 15: ROC curves of the k-Center classifier for different values of
Detection Rate in %
False Alarm Rate in %
Figure 16: Comparison of the best configurations of the different approaches.
In Figure 16 we compared the best configurations of different approaches
based on the previous experiments. The combined classifier was configured
as follows: AABB classifier with
, k-Center classifier with
, NN classifier with
and Stide with
and a mismatch threshold
of 0.55.
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6 Conclusion and Future Work
Compared to the best configuration of the Stide approach for the ADFA-LD
dataset, we could show that our n-gram probability graph approach can keep
pace even though it uses really simple classifier and features.
At the moment we have not yet been able to confirm our hypothesis from
section 3. However, the current results are promising in a way that we believe
further research is worthwhile.
Further work should be done by testing more complex classifiers: One Class
Support Vector Machine [
], Support Vector Data Description [
] or even
types of artificial neural networks. In addition we want to dive deeper into fea-
ture engineering by testing more features extracted from the underlying graphs
like out and in degree centralities, different quantiles of transition probabilities
and transition frequencies just to name a few. We want to examine the impact
of different normalization techniques and the principal component analysis.
In a second step, we also want to check whether the results can be improved
if not only the system calls, but also their metadata and parameters such as user
ids, process names, file handles etc. are taken into account and represented in
the graph.
This work was partly funded by the German Federal Ministry of Education and Re-
search within the project Explicit Privacy-Preserving Host Intrusion Detection System
(EXPLOIDS) (BMBF 16KIS0522K) and Competence Center for Scalable Data Ser-
vices and Solutions (ScaDS) Dresden/Leipzig (BMBF 01IS14014B).
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... The data set was not published. In [10] older and new algorithmic approaches were compared that evaluate sequences of system calls. It was observed that both the detection and the false alarm rates of the different approaches could not be improved beyond a certain value. ...
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Conference Paper
Intrusion detection systems are generally tested using datasets compiled at the end of last century, justified by the need for publicly available test data and the lack of any other alternative datasets. Prominent amongst this legacy group is the KDD project. Whilst a seminal contribution at the time of compilation, these datasets no longer represent relevant architecture or contemporary attack protocols, and are beset by data corruptions and inconsistencies. Hence, testing of new IDS approaches against these datasets does not provide an effective performance metric, and contributes to erroneous efficacy claims. This paper introduces a new publicly available dataset which is representative of modern attack structure and methodology. The new dataset is contrasted with the legacy datasets, and the performance difference of commonly used intrusion detection algorithms is highlighted.
Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.
Conference Paper
Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal (or clean) data. In this paper we present a technique for detecting anomalies without training on normal data. We present a method for detecting anomalies within a data set that contains a large number of normal elements and relatively few anomalies. We present a mixture model for explaining the presence of anomalies in the data. Motivated by the model, the approach uses machine learning...