Machine Learning Based Ensemble Classifier for Android Malware Detection

International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.4, July 2023
DOI:10.5121/ijcnc.2023.15407 111
MACHINE LEARNING BASED ENSEMBLE
CLASSIFIER FOR ANDROID MALWARE DETECTION
P Sumalatha1
and G.S. Mahalakshmi2
1
Associate Professor, Bhoj Reddy Engineering College for Women,
Hyderabad, and Telangana, India.
2
Associate Professor, Anna University, Chennai, Tamilnadu, India.
ABSTRACT
Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to
proliferation of Android applications and widespread usage of smartphones, malware problem is causing
significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence
(AI), machine learning (ML) models are widely used for detection of Android malware. However, many of
the existing methods focused on static or dynamic data to train classifiers for malware detection. In this
paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the
malware detection models with highest accuracy are chosen to be part of stacking ensemble model. An
algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed
and implemented. We made three experiments with the same algorithm but three different datasets
reflecting features obtained through different modus operandi. Each dataset is found to have influence on
the performance of the models. However, in all experiments, the ensemble approach showed highest
performance. The proposed method can be used in improving security for Android devices and
applications.
KEYWORDS
Artificial Intelligence, Machine Learning, Anomaly Detection, Android Malware Detection
1. INTRODUCTION
Malware is malicious program that has capacity to infect systems and networks. Adversaries are
spreading malware for monetary and other benefits. This malware propagation is spread to
applications in all walks of life in cyberspace. Android platform is no exception. Of late ML
models have proved to be efficient due to their learning process in malware detection. However,
it is important to have a methodology that considers different aspects of the detection mechanism
[1]. Traditionally there are methods that concentrated on either static or dynamically extracted
information from Android applications for understanding and analysing towards detection of
presence of malicious programs. However, with the emergence of AI-based techniques such as
ML models, researchers attracted towards this learning based phenomena to have more scalable
approach in detection of such malicious programs and their propagation in Android platform.
There are many existing methods such as [5], [8], [13] that focused on ML models for malware
detection in Android environments. Milosevic et al. [5] focused on learning based approach with
feature extraction and ML models to automate malware detection process. Naser et al. [8]
proposed a method for the malware detection in Android devices by considering API calls and
also permissions along with ML models. Since malware influences permissions and causes API
calls, their method assumes significance. Kaijun et al. [13] studied different malware detection

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methods that involve in learning-based techniques and found that they could perform better than
traditional methods. Some researchers proposed tool, as explored in [16], [22], [24] for automatic
detection of malware using ML. Sercan et al. [16] proposed a detection a tool known as And
MFC that is meant for automatically finding malware and classify it in Android platform. Xiao et
al. [22] proposed and implemented a tool named AndroidHIV which is meant for malware
repackaging experiments that were meant for evading malware detection process. Fauzia et al.
[24] proposed a tool named PIndroid that is a system for detection of malware exploits ensemble
models in ML. Few hybrid approaches are also found in literature as studied in [7], [10] and [17].
Importantly different ensemble methods are covered in the literature as in [11], [24] and [31].
However, the novelty of our method in this paper is that the constituent selection is made with a
threshold-based approach. From the literature it is found that many of the existing methods
focused on static or dynamic data to train classifiers for malware detection. Moreover, the
ensemble models could improve performance but there is room for improving accuracy in
detection of malware. Our contributions in the paper are as follows.
1. We proposed an ML-based ensemble framework that could improve malware detection
performance in Android platform.
2. We proposed an algorithm named Stacking Ensemble for Automatic Android Malware
Detection (SE-AAMD). The ensemble method is based on carefully chosen and best
performing classifiers.
3. We made empirical study to evaluate performance of SE-AAMD and compared with existing
models. We found that our algorithm outperforms all constituent ML models. The remainder
of the paper is structured as follows. Section 2 reviews existing methods for finding presence
of malware in Android platform. Section 3 presents our stacking ensemble methodology
which has novel approach in constituent classifier selection. Section 4 presents experimental
results and Section 5 concludes our work.
2. RELATED WORK
This section reviews literature on different existing ML-based methods for malware detection.
Yerima and Khan [1] investigated on different ML models and their performance in malware
detection process. Potha et al. [2] proposed an ensemble method for improving efficiency in
malware detection. It is based on random ensemble phenomenon which exploits extrinsic training
instances along with many pre-determined instances. Their algorithm has mechanisms to find
final malware score for each instance through different methods and ensemble the classifiers.
Yerima et al. [3] proposed an APK analyser method to extract features and use them to train ML
classifier towards malware detection process. They found that Random Forest is an ensemble
approach showed better performance. Chen et al. [4] proposed a system known as Secure Droid
for improving security of mobile applications form malware problems. It extracts features and
performs ensemble learning approach to determine class labels. Milosevic et al. [5] focused on
learning based approach with feature extraction and ML models to automate malware detection
process. Kouliaridis et al. [6] investigated on different ML techniques and found that they are
feasible in detection of malware with good quality in detection results. Lilian et al. [7] followed a
hybrid approach that includes feature engineering and also an ensemble classification method for
detecting Android malware. They observed significance of feature engineering in improving
accuracy. Naser et al. [8] proposed a method for malware detection in Android devices by
considering API calls and also permissions. Since malware influences permissions and causes
API calls, their method assumes significance. Zhuo et al. [9] focused on malware detection in
Android with the help of malware related control flow graphs and also algorithms that learn from
given historical data. Alejandro et al. [10] incorporated novelty in their research by exploiting a
hybrid feature selection and fusion besides considering ensemble approach in detection of
malware.

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Pengbin et al. [11] proposed a dynamic approach in malware detection. It has provision for
dynamically extracted features and then use them to train ensemble classifiers towards improving
detection accuracy. Suleiman et al. [12] proposed a malware detection system for Android. It is
known as DroidFusion which has a classifier fusion approach at multiple levels towards
improving focus on malware detection process. Kaijun et al. [13] studied different malware
detection methods that involve in learning-based techniques and found that they could perform
better than traditional methods. Shahzeb et al. [14] focused on SDN based systems in distributed
environment. In such environment, their focus was on building an ensemble classifier based on
deep learning to detect anomalies. Dehkordy et al. [15] investigated on different aspects of
datasets including imbalanced datasets that are to be treated differently. With such imbalanced
data, they proposed a ML based method for malware detection. Sercan et al. [16] proposed a
detection tool known as AndMFC that is meant for automatically finding malware and classify it
in Android platform. Surendran et al. [17] proposed a TAN-assisted approach with hybrid
methodology to detect malware. Their methodology has revealed the significance of TAN usage
in the process of malware detection. Rana et al. [18] studied ML models with the view of
malware detection in mind. Their methodology showed that it could have improved detecting
performance. Daoudi et al. [19] investigated on possible reproducibility of existing ML models
that are used for detecting malware. Their research could find that the models can be reused
proper environment and datasets. Zhenxiang et al. [20] investigated network traffics that reflect
high imbalances towards detection of malware. Their methodology used ML models in such
datasets.
Xin et al. [21] focused on the notion of information fusion in order to improve malware detection
probability using ML techniques. Their empirical study is made on parallel processing
environment a found the improvement in faster processing of data. Xiao et al. [22] proposed and
implemented a tool named AndroidHIV which is meant for malware repackaging experiments
that were meant for evading malware detection process. Zainab et al. [23] focused on ML models
for malware detection and performed quantification of evasion attacks from adversaries. Fauzia et
al. [24] proposed a tool named PIndroid that is a system for detection of malware exploits
ensemble models in ML. Mohammed et al. [25] defined a methodology named as DL-Droid that
makes use of deep learning techniques to detect malware automatically in Android platform.
Other important research contributions include Anastasia tool [26], pragmatic detection function
[27], learning-based framework [28], Tree-based ensemble [29], ensemble optimization [30] and
stacking ensemble [31]. In [33] and [34], it is observed that deep learning based approaches do
have their impact on solving problems in various domains. From the literature it is found that
many of the existing methods focused on static or dynamic data to train classifiers for malware
detection. Moreover, the ensemble models could improve performance but there is room for
improving accuracy in detection of malware.
3. MATERIALS AND METHODS
We proposed a ML-based methodology for malware detection. It is an intelligent approach that
has novel constituent selection procedure for ensemble classifiers. Android malware dataset is
used for the empirical study with three kinds of focus on the data.
3.1. The Framework
We proposed a framework for automatic detection of malware with ensemble learning approach.
The outline of the proposed framework, known as Stacked Ensemble Framework (SEF), is
provided in Figure 1 and its complete architecture is illustrated in Figure 2. It is based on ML
models along with ensemble and threshold-based constituent selection method to improve

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detection performance. The framework emphasizes more accurate means of making ensemble
with stacking approach after choosing best constituent classifiers.
Figure 1. Outline of our methodology
We have considered many ML classifiers for the empirical study. It is data-driven approach
where supervised learning plays crucial role in learning from training data and perform detection
process based on the learned knowhow. We have set a threshold of 90% accuracy to be qualified
for classifiers to participate in ensemble model. This is meant for ensuring higher level of
accuracy in detection process. The stacking ensemble model is used as it could lead to better
performance in malware detection.
Figure 2. Overview of proposed Stacked Ensemble Framework (SEF)

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Our method has merits in terms of choosing constituent classifiers and making a stacking
ensemble to realize SEF. There is feature selection process that is based on three kinds of data
used for three different experiments. With all the three experiments, feature selection is made in
order to improve quality in the learning process. Only selected features are used to train the
prediction models. While making ensemble of different classifiers, they are filtered based on their
performance with the given threshold that is >90%. In Figure 2, classifier refers to an algorithm
while model refers to a trained or learned model. Since the training data is labelled data, the
model has discriminative power. The classification results given by constituent models are finally
determined with stacking ensemble approach for more efficient detection of malware. Finally,
our framework assigns class label for each test instance as either NORMAL or MALWARE
based on the intelligence the proposed model gains.
Figure 3. Training and Testing process
As illustrated in Figure 3, the given Android malware data (training data) is undergoing pre-
process to improve quality of data. Then it has feature extraction and feature selection that is
crucial for leveraging learning accuracy. Then the training is given to classifier based on the
selected features only. With regard to testing phase, the given test data contains no class labels. In
other words, it is the data to be used by the proposed system to detect presence of Android
malware. In other words, class labels are to be predicted by the system. A learned model is used
to work on the test data to classify given samples into MALWARE or NORMAL.
3.2. Dataset Description
Datasets used in this research are obtained from [32]. The data is named CICAndMal2017. It has
both benign and malware samples. The total number of samples available in the online resource
is 10854. The dataset has 215 features. Some of the important features are shown in Table 1.

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Table.1 some of the important features in the dataset
S.no Feature
1 Transact,
2 android.os.Binder,
3 SEND_SMS,
4 Ljava.lang.Class.getCanonicalName,
5 Ljava.lang.Class.getMethods,
6 Ljava.lang.Class.cast,
7 READ_CONTACTS,
8 DEVICE_POWER,
9 HARDWARE_TEST,
10 ACCESS_WIFI_STATE,
11 WRITE_EXTERNAL_STORAGE,
12 ACCESS_FINE_LOCATION,
13 SET_WALLPAPER_HINTS,
14 SET_PREFERRED_APPLICATIONS,
15 WRITE_SECURE_SETTINGS,
16 class
3.3. Feature Selection
Featue selection plays crucial role in improving learninig quality in supervised models. In this
paper we applies feature extraction procedure based on the given kind of dataset for each
experiment. Then from the chosen features, class label prediction contribution is used to analyze
importance of each feature. Based on the feature importance, highly contributing features are
selected for training classifiers.
Table.2 The selected features after performing feature selection
S.no Feature
1 Transact,
2 android.os.Binder,
3 SEND_SMS,
4 Ljava.lang.Class.getMethods,
5 Ljava.lang.Class.cast,
6 READ_CONTACTS,
7 ACCESS_WIFI_STATE,
8 ACCESS_FINE_LOCATION,
9 SET_WALLPAPER_HINTS,
10 WRITE_SECURE_SETTINGS,
The selected feature are taken as input to the models and the proposed models will be trained
with respect to classified features and class label. The prediction will be done based on the model
which shows better prformance after features selection methods had implemented.
3.4. Stacking Ensemble Approach
In our framework SEF, stacking ensemble approach is used. It is an approach that has provision
to combine many constituent ML models or regression models with the help of a meta-regressor.
The constituent ML models are trained with entire training data as per feature selection process.
Then the feature outcomes of those models are used to train the meta-regressor in order to
enhance detection accuracy. It mainly involves combining the predictions from multiple machine

117
learning models on the same dataset, like bagging and boosting. As we import the module called
stacking CVC classifier from the mlxtend package which represents extensions for ensemble
techniques in data science tasks. The main benefit of stacking approach is that it can harness the
capabilities of range of well-performing models on a classification or regression task and make
predictions that have better performance than any single model in the ensemble.
3.5. Algorithm Design
We proposed an algorithm known as Stacking Ensemble for Automatic Android Malware
Detection (SE-AAMD) is proposed and implemented. We made three experiments with the same
algorithm but three different datasets reflecting features obtained through different modus
operandi. Each dataset is found to have its influence on the performance of the models. Our
approach to constituent models selection is illustrated in Figure 4.
Figure 4. Shows the baseline models chosen based on given criterion
There are different ML models considered as input to the selection process. However, with
empirical study, each model is evaluated and if the model performs with accuracy >=90%, only
such models are chosen for ensemble.

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Algorithm:Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)
Inputs:
Android malware dataset D
Intelligent prediction models in pipeline P
Output:
Malware detection results R’
1. Begin
2. Initialize models map M
3. Initialize ensemble map E
4. FFeatureExtraction(D)
5. (T1, T2)DataPreProcess(D)
Training ML Models
6. For each constituent model p in P
7. tTrain p using F
8. Add p and t to M
9. End For
Testing ML Models
10. For each map entrym in M
11. Test model for T2 with F
12. Add model results to R
13. Compute confusion matrix
14. Updated E with model and R
15. End For
Ensemble Decision
16. R’ApplyStackingModel(E)
17. Display R’
18. End
Algorithm 1. Hybrid Ensemble and Feature Engineering for Stroke Prediction (HEFE-SP) algorithm
As presented in Algorithm 1, it takes patient brain stroke dataset D and selected stroke prediction
classifiers B as pipeline and produces output in the form of stroke classification results R’. The
given dataset is split into 80% and 20% training set and testing set respectively. Both training and
test data are subjected to feature engineering using our proposed algorithm HMA-FE. The
selected baseline models are trained and then they are used as ensemble model. The ensemble
model results in final predictions based on weighted majority voting approach. Stacking
ensemble used in SE-AAMD harnesses capabilities of models while making final predictions. It
also helps in improving prediction accuracy. Its limitation is that prediction accuracy is based on
capabilities of constituent models used in the ensemble. Moreover the constituent models we
chose for ensemble or carefully selected with some criteria could improve its performance when
comapared with the state of the art.
3.6. Evaluation Methodology
Performance of different prediction models is evaluated using confusion matrix-based measures
like precision, recall, F-measure and accuracy. These measures are based on the predictions of the
models in terms of number of true positives (TP), number of true negatives (TN), number of false
positives (FP) and number of false negatives (FN). A performance metric known as AUC (Area
Under Curve) is used for evaluation of performance. It is based on two metrics known as True
Positive Rate (TPR) and False Positive Rate (FPR).
TPR =
𝑇𝑃
𝑇𝑃+𝐹𝑁
(1)

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FPR =
𝐹𝑃
𝐹𝑃+𝑇𝑁
(2)
Computation of performance metrics is carried out as in Eq. 1 and Eq. 2. These metrics hold a
value between 0.0 and 1.0 reflecting the least and highest performance. More in value for TPR or
FPR indicates higher performance.
4. RESULTS AND DISCUSSION
This section presents results of our three experiments. Each experiment is given with different
data but includes constituent methods and stacking ensemble as well. Each experiment has shown
that the stacking ensemble approach is performing better than existing models.
4.1. Results of First Experiment
This section shows results of first experiment made for automatic android malware detection
process.
Figure 5. Shows AUC of constituent models
As presented in Figure 5, the AUC of different constituent models in the first experiment is
provided. Higher in AUC indicates better performance.
Figure 6. Show AUC analysis for each constituent model and stacking ensemble model

114
As presented in Figure 6, the AUC analysis of different constituent models in the first experiment
is provided along with stacking ensemble model.
Figure 7. ROC performance of constituent models
As presented in Figure 7, ROC plot that makes use of TPR and FPR values for experiment 1 is
provided to show performance comparison. Higher ROC indicates better performance.
Figure 8. Shows ROC of least predicted model and the proposed meta-regressor model
As presented in Figure 8, the proposed meta-regressor model showed the highest performance in
terms of ROC in the first experiment.

115
Figure 9. Performance comparison between constituent models and ensemble model in first experiment
As presented in Figure 9, the AUC performance which reflects malware detection accuracy is
compared among all constituent models and ensemble model. The least AUC is exhibited by DT
with 73.94%. RF showed 86.49%, CatBoost 89.83,LGBM 88.24%, ExtraTrees 87.95% and MLP
89.7%. However, the highest accuracy is achieved by stacking ensemble model proposed with
89.86% accuracy. The performance improvement of stacking ensemble model is due to the fact
that it exploits knowledge of constituent models to leverage prediction performance.
4.2. Results of Second Experiment
This section shows results of second experiment made for automatic android malware detection
process.
As presented in Figure 10, the AUC of different constituent models in the second experiment is
provided.
0.8649
0.7394
0.8983 0.8824 0.8795 0.8947 0.8986
0
0.2
0.4
0.6
0.8
1
Performance(%)
Model
PERFORMANCE COMPARISON
RF -> AUC DecisionTree -> AUC CatBoost -> AUC
LGBM -> AUC ExtraTrees->AUC MLP->AUC
scl->AUC

116
As presented in Figure 11, the AUC analysis of different constituent models in the second
experiment is provided along with stacking ensemble model.
As presented in Figure 12,ROC plot that makes use of TPR and FPR values for experiment 2 is
provided to show performance comparison.

117
terms of ROC in the second experiment.
Figure 14. Performance comparison between constituent models and ensemble model in second experiment
compared among all constituent models and ensemble model. The least AUC is exhibited by DT
with 93.13%. RF showed 93.81%, CatBoost 96.27, LGBM 96.62%, ExtraTrees 96.29% and MLP
0.9381
0.9313
0.9627
0.9662
0.9629
0.9537
0.9672
0.91
0.92
0.93
0.94
0.95
0.96
0.97
Performance(%)
model
Results of Second Experiment

118
4.3. Results of Third Experiment
This section shows results of third experiment made for automatic android malware detection
process.
As presented in Figure 15, the AUC of different constituent models in the third experiment is
provided.
As presented in Figure 16, the AUC analysis of different constituent models in the third
experiment is provided along with stacking ensemble model.

119
As presented in Figure 17, ROC plot that makes use of TPR and FPR values for experiment 3 is
provided to show performance comparison.
terms of ROC in the third experiment.

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Figure 19. Performance comparison between constituent models and ensemble model in third experiment
compared among all constituent models and ensemble model. Least AUC is exhibited by DT with
73.94%. RF showed 96.49%, CatBoost 89.83, LGBM 88.24%, ExtraTrees 87.95% and MLP
5. CONCLUSION
In this paper, we propose an ensemble model with intelligent methods that are empirically
selected. Only the malware detection models with the highest accuracy are chosen to be part of
stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android
Malware Detection (SE-AAMD) is proposed and implemented. We made three experiments with
the same algorithm but three different datasets reflecting features obtained through different
modus operandi. Each dataset is found to have its influence on the performance of the models.
However, in all experiments, the ensemble approach showed the highest performance. The
proposed ensemble model exhibited highest accuracy with 96.72% which is better than many ML
models such as RF, DT, CatBoost, LGBM, ExtraTrees and MLP. The proposed method can be
used in improving security for Android devices and applications. In future we intend to
investigate on deep learning models for Android malware detection.
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AUTHORS
Sumalatha Potteti is working as Assistant Professor at Bhoj Reddy Engineering
College for Women, Hyderabad, Telangana (State), India. She has received B.Tech
(CSE), M.Tech (CSE) Degree in Computer Science and Engineering. Her main
research interest includes Machine Learning and Intrusion Detection.
Dr. G.S. Mahalakshmi is working as Associate Professor at Anna University,
Guindy, Chennai, Tamilnadu (State), India. She has received B.E(CSE), M.E(CSE),
Ph.D. Degree in Computer Science and Engineering. Her main research interest
includes Artificial Intelligence, Machine Learning, Natural Language Processing
knowledge representation and Text Mining.

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