Mobile Family Detection through Audio Signals Classification
Rosangela Casolare
1
, Giacomo Iadarola
2
, Fabio Martinelli
2
, Francesco Mercaldo
2,3
and Antonella Santone
3
1
Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy
2
Institute of Informatics and Telematics, National Research Council of Italy, Pisa, Italy
3
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
Keywords:
Malware, Audio, Android, Machine Learning, Deep Learning, Security, Classification.
Abstract:
Nowadays smartphones, and generically speaking mobile devices, allow users a plethora of tasks in total mo-
bility for instance, from checking the balance on the bank account to distance learning. In this context it is
really critical the detection of malicious behaviours, considering the weaknesses of the current antimalware
mechanisms. In this paper we propose a method for malicious family detection exploiting audio signal pro-
cessing: in fact, an application is converted into an audio file and then is processed to generate a feature vector
to input several classifiers. We perform a real-world experimental analysis by considering a set of malware
targeting the Android platform i.e., 4746 malware belonging to 10 families, showing the effectiveness of the
proposed approach for Android malicious family detection.
1 INTRODUCTION
In the last years, there was a huge spread of mobile de-
vices like smartphones and tablets, which became the
principal target of attacks, because these devices con-
tain a lot of sensitive, financial and personal informa-
tion. Among various software systems present in mo-
bile devices, Android is the most popular and also the
most diffuse; since Android is an open source system,
it arouses more interest from malicious people as it al-
lows you to create customized systems by rebuilding
the source code (Enck et al., 2014). Furthermore, this
operating system allows to install applications from
third-party stores as well as from the official ones;
thus, the users, who lack adequate knowledge of the
dangers to which they are exposed, are subject to at-
tacks launched by applications downloaded from un-
official stores, which are less reliable. Nevertheless,
the presence of malicious applications cannot be ex-
cluded even in the official stores (i.e., Google Play
Store) (Nguyen et al., 2020).
For this reason, the Android environment results
to be the most attacked by cybercriminals (Canfora
et al., 2018).
During May 2020, around 430,000 malware at-
tacks were detected on Android devices, counting an
increase of 3.6% compared to the previous month. In-
stead, in August 2020, it was observed a growth of
6.26% compared to the previous month
1
.
In this regard, we propose an approach based on
the detection of malware families in the Android en-
vironment, which consists of the conversion of an An-
droid application into an audio file. Then, we extract
from the audio file a series of numerical features that
are used to understand which family the application
belongs to.
Among the most applied analysis techniques in
the literature, we decided to explore a group of
them and to adopt four different supervised classifica-
tion algorithms, belonging to the Machine Learning
(Stochastic Gradient Descent, Random Forest) and
Deep Learning fields (two different model structures
of Multy-layer Perceptron).
The paper’s organization is the following: in sec-
tion 2 is described the proposed method to analyse the
audio signals and make the malware family detection,
starting from the conversion of an Android applica-
tion in an audio file, which its features will be used to
classify the family belonging; in section 3 is described
the considered dataset, showing the effectiveness of
the experimental analysis executed on it; in section 4
current state-of-the-art literature is analyzed and dis-
1
https://news.drweb.com/show/review/?i=13991&lng=en
Casolare, R., Iadarola, G., Martinelli, F., Mercaldo, F. and Santone, A.
Mobile Family Detection through Audio Signals Classification.
DOI: 10.5220/0010543504790486
In Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021), pages 479-486
ISBN: 978-989-758-524-1
Copyright
c
 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
479

cussed and, finally, in section 5 conclusion and future
research plans are presented.
2 AUDIO SIGNALS FOR
MALWARE FAMILY
DETECTION
In this section, we present the method we propose for
mobile family detection. In a nutshell, we convert an
Android application into an audio file and we extract
a series of numerical values (i.e., the features) from
the audio file. Then, the features represent the input
for a supervised classifier (previously trained) aimed
to predict the belonging malware family. In detail
the proposed method considers two distinct phases:
Training (shown in Figure 1) and Testing (shown in
Figure 2).
The Training phase, depicted in Figure 1, is aimed
to build a model for the malicious family prediction.
We start with a malware dataset (composed by mali-
cious Android application) and the relative family la-
bel i.e., the detail about the malicious family for each
sample involved. Subsequently, we extract from each
Android application (stored in the apk file format) the
executable file (i.e., the dex file), containing the bi-
nary of the application (we discard from the analysis
all the application resources as, for instance, images
and sounds). To convert binary (i.e., the dex file) we
consider binary bytes forming a digitized raw signal,
then we convert the raw signal into wav. In detail
to generate a wav file from dex file, we developed a
function aimed to firstly generate a wav header, sub-
sequently the dex file is open and each byte of this
file is converted in wav. For this task, we resort the
wave module
2
available in Python; in particular, we
invoked the open and the writeframes methods: the
first one to open the file, while the second one for wav
file writing. By exploiting the setparams methods, we
also considered the following parameters for the wav
generation: the number of audio channels equal to 1
(mono i.e., with one input which is distributed equally
by the left and right speakers), the sample width set to
n bytes with n = 1, the frame rate set to 32768, and the
number of frames set to 0 and without compression.
Once obtained the audio samples related to each
Android sample in the malicious dataset, a set of fea-
ture is directly computed on the audio sample.
In detail, the following features are computed:
• Chromagram: this feature is related to a chroma-
gram representation automatically gathered from
a waveform;
2
https://docs.python.org/3/library/wave.html
• Root Mean Square: this feature (i.e., RMS) is re-
lated to the value of the mean square of the root
that is obtained for each audioframe that is gath-
ered from the sound sample under analysis;
• Spectral Centroid: this feature is symptomatic of
the “centre of mass” for a sound sample that is
obtained as the mean related to the frequencies of
the audio;
• Bandwidth: it is related to the bandwidth of the
spectrum;
• Spectral Rolloff : it is expressed as the frequency
related to a certain percentage of the total spectral
of the energy;
• Zero Crossing Rate: it is expressed as the ra-
dio belonging to the sign variation relating to the
sound samples;
• Mel-Frequency Cepstral Coefficients: this feature
(i.e., MFCC), ranging from 10 to 20 different nu-
merical features, is devoted to represent the shape
of a spectral envelope;
• Zero Crossing Rate: this value is related to the rate
of an audio time series;
• Poly: it is computed as the fitting coefficients re-
lated to an nth-order polynomial;
• Tonnetz: it is computed from the the tonal cen-
troid.
Once we obtained the feature vectors from the .wav
files, we export them to .csv files, where each row
contains the feature values for each app under anal-
ysis with the relative label of the belonging family.
Subsequently, we set the parameters for the clas-
sification algorithms (i.e., model setting in Figure 1).
We adopt four different supervised classification al-
gorithm. In detail, we experiment the effectiveness of
following models:
• Stochastic Gradient Descent (SGD): uses stochas-
tic gradient descent that minimizes a chosen loss
function with a linear function. The algorithm
approximates a true gradient by considering one
sample at a time, and simultaneously updates the
model based on the gradient of the loss function;
• Random Forest: an ensemble classifier obtained
from the bagging of decision trees. It consists of
hundreds of thousands of decision trees. It falls
under those ensemble learning algorithms, that is,
algorithms that use multiple machine learning al-
gorithms to get more precise predictions. The
number of trees depends on the nature of the train-
ing set and other parameters such as the number
of classes, the number of beans and the maximum
depth;
SECRYPT 2021 - 18th International Conference on Security and Cryptography
480

Figure 1: Training.
• MLP 1: it is model of computation based on bi-
ological neural networks. A neural network is an
interconnection of a group of nodes called neu-
rons. An artificial neural network receives exter-
nal signals on a layer of input nodes (processing
units), each of which is connected with numerous
internal nodes, organized in several layers. Each
node processes the received signals and transmits
the result to subsequent nodes. In detail we ex-
ploit a multi-layer perceptron (MLP) algorithm
with backpropagation, a class of feedforward ar-
tificial neural network. We consider two differ-
ent networks exploiting the MLP architecture: the
MLP 1 model consists of three layers of nodes:
an input layer, a hidden layer and an output layer.
This model consider 100 neurons for the hidden
layer, by exploiting the ReLU activation and the
Adam solver;
• MLP 2: this model is based, as the MLP 1, the
MLP algorithm with backpropagation. Differ-
ently from the MLP 1 model, the MLP 2 one is
composed by an input layer, three hidden layers
each one considering 100 neurons and the output
layer (while the MLP 1 considers only one hidden
layer).
Four different models are considered for conclusion
validity i.e., to demonstrate that the proposed feature
set, obtained from audio samples, can be effective in
the discrimination of different malicious families.
Once built the predictive model, its effectiveness
is evaluated in the Testing phase, shown in Figure 2.
The idea of the Testing phase is the evaluation of
the effectiveness of the model built in the Training
phase. For this reason, considering an application
not considered in the model generation (i.e., app un-
der analysis in Figure 2), its dex file is obtained from
the apk one and it is converted into an audio sample.
Thus, from the audio sample the features are extracted
and then are considered as input for the model that
will generated a prediction (i.e., malicious family in
Figure 2).
3 STUDY DESIGN AND
EXPERIMENTAL ANALYSIS
We design a study composed by two steps: the first
one is the descriptive statistics, aimed to provide a
graphical impact about the feature value distributions
for all the involved families and the second one is the
classification results, devoted to confirm the effective-
ness of the proposed model for the mobile family de-
tection task.
With regard to the descriptive statistics we exploit
boxplots, a method for graphically depicting groups
of numerical data through their quartile, to display
variation in samples of a statistical population without
making any assumptions of the underlying statistical
distribution.
The classification analysis is aimed to compute
a set of well-known metrics to provide a numerical
measurement to evaluating the performances of the
proposed models.
3.1 The Real-world Dataset
As stated into the introduction, we consider a real-
world dataset composed by 4796 Android malicious
applications belonging to 10 different families, as
shown in Table 1.
The dataset considered in the experiment was
gathered from three different repositories: the first
one is the Drebin dataset (Arp et al., 2014; Michael
et al., 2013), a very well-known collection of malware
largely considered by malware analysis researchers,
including the most widespread Android families. The
malware dataset is freely available for research pur-
poses
3
. The second malware repositoty is Conta-
gio Mobile
4
, a web site containing malicious sam-
ples with the relative technical report about the ma-
licious behaviour. The third malicious repository we
exploited is the Android Malware repository (AMD)
(Iadarola et al., 2020).
3
https://www.sec.cs.tu-bs.de/
∼
danarp/drebin/
4
http://contagiominidump.blogspot.com/
Mobile Family Detection through Audio Signals Classification
481

Figure 2: Testing.
Table 1: The real-world malicious dataset involved in the study.
Family Description Inst. #
accutrack it tracks down the GPS location of the device on which it was installed R 500
airpush it aggressively pushes advertising content to the device’s notification bar R 500
basebridge it sends SMS and personal information R, U 600
droidkungfu it uses exploits in its attempt to root a device to install other applications R 667
fakeinstaller it sends SMS messages to premium-rate services S 606
hummingbad it establishes a persistent rootkit and installs fraudulent applications R 500
judy an auto-clicking adware relying on the communication with its C&C server R 84
opfake it hides its presence by installing the Opera browser and can monitor SMS S 610
overlay a fake bank application using overlay technique to steal user credentials U 56
plankton it installs a JAR file obtained from an external server U 623
The considered malware dataset consists of 10 An-
droid malicious families characterized by different in-
stallation methods (column Inst. in Table 1): (i) stan-
dalone (i.e., S in Table 1), applications that intention-
ally include malicious functionalities; (ii) repackag-
ing (i.e., R in Table 1), known and common (legit-
imate) applications that are first disassembled, then
the malicious payload is added, and finally are re-
assembled and distributed as a new version (of the
original application); and (iii) update attack (i.e., U in
Table 1), applications that initially do not show harm-
ful behaviors and download an update containing the
malicious payload, at runtime.
In detail the basebridge, the droidkungfu, the fake-
installer, the opfake and the plankton families were
obtained from the Drebin dataset, the hummingbad,
the judy and the overlay ones from the Contagio Mo-
bile website. The accutrack, airpush families were
gathered from the AMD dataset.
The malware dataset is also partitioned according
to the malware family (Zhou and Jiang, 2012).
We analyzed the dataset with the VirusTotal ser-
vice
5
, a web service able to run 61 commercial and
free antimalware: this analysis confirmed that the ma-
licious applications were actually recognized as mal-
ware.
In Table 1 we indicate also the details about the
number of samples considered for each malware fam-
ily (i.e., column # in Table 1).
5
https://www.virustotal.com/
For each application of the dataset we gathered the
audio sample and the feature set with the procedure
explained in the previous section.
3.2 Descriptive Statistics
Figure 3 shows the box-plots related for the spectral
chromogram features. For reason space we show only
this plot, but similar considerations can be done for
the remaining ones.
In Figure 3 each boxplot is related to a single fam-
ily. On the top of each boxplot we indicate the fam-
ily name and the median value, while below, for each
boxplot, from the left the value of the first quartile,
the average and the value of the third quartile.
From the Spectral Centroid boxplot in Figure 3, it
emerges that the values for this feature for the applica-
tion to the malicious dataset ranging into different val-
ues. For instance, the numerical values for the accu-
track family are ranging in a smaller range if com-
pared to the airpush family. A similar trend is exhib-
ited by the droidkungfu, the plankton and the hum-
mingbad families. For this reason, from this analysis,
it seems that the spectral centroid can not be of in-
terest for the discrimination of these families. Differ-
ently, the overlay family assumes values whose first
quartile is greater than the third quartile of all other
families, making this feature very discriminatory in
identifying this family. The judy family boxplot in
the part between the first quartile and the average dif-
SECRYPT 2021 - 18th International Conference on Security and Cryptography
482

Figure 3: Box-plots for the Spectral Centroid feature.
ferent values overlapping with those of other families,
but from the average up to the third quartile there is
no overlap with any other family, for this reason the
feature can be considered discriminating enough to
distinguish this family from others. Also the opfake
family boxplot is of interest in fact, there is only a
slight overlap with some values near the third quartile
with the first quartile of the remaining families.
Obviously, the more the boxplots of each fam-
ily are not superimposed, the higher the probability
that the models will be able to correctly discriminate
the different families. From this visual analysis it
emerges that this feature can actually be valid to dis-
tinguish some families from others, but as has been
said for some families there is overlap. This is the
reason why we consider a set of features, in order to
increase this possibility.
3.3 Classification Analysis
With regard to the classification analysis, for differ-
ent metrics are exploited to measure the effectiveness
of the proposed method in Android family detection:
Precision, Recall, F-Measure and Accuracy.
Table 2 shows the classification results.
As emerges from the results in Table 2 the model
obtaining the best performances is MLP 2 with an
average accuracy for family identification equal to
0.988.
Also the MLP 1 and the Random Forest classifica-
Mobile Family Detection through Audio Signals Classification
483

Table 2: Classification results.
Model Precision Recall F-Measure Accuracy
SGD 0.583 0.611 0.586 0.779
MLP 1 0.831 0.833 0.831 0.974
Random Forest 0.905 0.905 0.905 0.986
MLP 2 0.907 0.907 0.907 0.988
tion algorithms obtain interesting performances with
an average accuracy equal to 0.974 for the MLP 1 and
equal to 0.986 for the Random Forest.
In Figure 4 we show the ROC curve plot relating
to the accutrack family. The ROC curve is created by
plotting the True Positive Rate (TPR, fraction of true
positives) versus the False Positive Rate (FPR, frac-
tion of false positives) at various threshold settings.
In Figure 4 the green line is related to the Random
Forest algorithm, the orange one to the MLP 1 model,
the purple to the SGD model and, the pink one to the
MLP 2 network.
As shown from the ROC Area in Figure 4 with
the exception of the SDG model, the remaining ones
exhibit equally good performances.
Starting from this results, we focus our analysis
on the model obtaining the best results in the classi-
fication analysis i.e., the deep learning one (MLP 2
in Table 2). For understand the performances of the
MLP 2 model at a family grain, in Figure 5 we show
the confusion matrix.
All the family are generally correctly detected as
belonging to the right malicious family. We highlight
67 (on a total of 667 samples of this family) droid-
kungfu samples erroneously detected as belonging to
the accutrack family and 85 (on 500 samples analy-
sis of this family) accutrack samples predicted as be-
longing to the droidkungfu one. These two examples
represent the main cases of misclassifications. This
aspect is also visible from the descriptive analysis,
where in the boxplots shown in Figure 3 we high-
lighted the overlapping between the droidkungfu and
the hummingbad malware families.
4 RELATED WORK
The proposed techniques rely on the vivid research
branch that studies signal features to detect malware.
In this paper, we exploit the audio signal, but more ap-
proaches were proposed in the literature, such as tex-
ture (Nataraj et al., 2011), network (Kim et al., 2018)
or behavioural features analysis (Popli and Girdhar,
2019).
The paper (Farrokhmanesh and Hamzeh, 2019)
proposes a similar technique, which extracts the pro-
gram’s bytes and converts them to an audio signal.
In detail, the byte of executable files are converted to
musical notes (MIDI note) and then audio files are
generated. Then, audio features such as MFCC and
Chromagram are used to classify music and machine
learning classifiers (KNN) is applied.
The main issues of such approaches regard the
size of byte sequences to analyze. Most of the time,
the static analyses required are time-consuming and
computationally expensive. The paper (Bakhshinejad
and Hamzeh, 2017) presents an approach that tries to
mitigate this problem by applying compression algo-
rithms to the sequences to study. Similarly, the ap-
proach proposed by Jerome Q. et al. (Jerome et al.,
2014) works directly on the binary sequences, by ex-
tracting k-gram and classifying the malware with an
SVM.
The method presented in (Vasan et al., 2020)
converts malware binaries into colour images, and
then use a CNN model, pre-trained on the ImageNet
dataset, to distinguish between malware and benign
samples. The approach proposed in (Iadarola et al.,
2021) applies a similar methodology but improves the
robustness of the classification, by analysing also the
inference phase, exploiting the use of a Grad-CAM.
The approach proposed in (Azab and Khasawneh,
2020) exploits the use of both audio signal process-
ing and image classification techniques. The initial
program’s bytes are cast to audio signals, and then
applied Fourier Transformation to convert the signal
from time-domain to frequency-domain and generate
spectrograms. Then, the spectrograms are analysed
by a Convolutional Neural Network such as a stan-
dard image-classification task.
5 CONCLUSION AND FUTURE
WORK
Mobile malware is continuously plaguing users, that
are unaware of the malicious behaviour that silently
are able to perpetrate harmful action as, for instance,
sending sensitive and private information (as, for in-
stance, the samples belonging to the accutrack fam-
ily) but also to install undesired apps (behaviour ex-
hibited by the droidkungfu and hummingbad fami-
SECRYPT 2021 - 18th International Conference on Security and Cryptography
484

Figure 4: ROC curve for the accutrack family.
Figure 5: Confusion Matrix for the MLP 2 model.
lies). In this paper we propose a technique for mo-
bile malware classification into malicious belonging
family. In detail we propose the analysis of an au-
dio stream, obtained from the Android application un-
der analysis, to extract a set of numerical features.
These features are the input for several machine learn-
ing classifiers that we evaluate with more than 4500
malware targeting the Android environment. We ob-
tain an accuracy equal to 0.988 using a deep learn-
ing model designed by authors, showing that the pro-
posed method can be effective for Android malware
family detection. As future work, we plan to try to
localise inside the audio wave the frame related to the
malicious behaviour. Moreover, we will experiment
the proposed method by using a dataset composed by
iOS applications.
ACKNOWLEDGEMENTS
This work has been partially supported by MIUR -
SecureOpenNets, EU SPARTA, CyberSANE and E-
CORRIDOR projects.
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