Botnet detection using ensemble classifiers of network flow

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 3, June 2020, pp. 2543~2550
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i3.pp2543-2550  2543
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Botnet detection using ensemble classifiers of network flow
Zahraa M. Algelal1
, Eman Abdulaziz Ghani Aldhaher2
, Dalia N. Abdul-Wadood3
,
Radhwan Hussein Abdulzhraa Al-Sagheer4
1,2,4
Department of Computer Science, Faculty of Education for Girls, University of Kufa, Iraq
3
College of Medicine, University of Bagdad, Iraq
Article Info ABSTRACT
Article history:
Received Mar 19, 2019
Revised Oct 26, 2019
Accepted Nov 22, 2019
Recently, Botnets have become a common tool for implementing and
transferring various malicious codes over the Internet. These codes can be
used to execute many malicious activities including DDOS attack, send
spam, click fraud, and steal data. Therefore, it is necessary to use Modern
technologies to reduce this phenomenon and avoid them in advance in order
to differentiate the Botnets traffic from normal network traffic. In this work,
ensemble classifier algorithms to identify such damaging botnet traffic.
We experimented with different ensemble algorithms to compare and analyze
their ability to classify the botnet traffic from the normal traffic by selecting
distinguishing features of the network traffic. Botnet Detection offers
a reliable and cheap style for ensuring transferring integrity and warning
the risks before its occurrence.
Keywords:
Botnet
Ensemble
Machine learning
Network flow
Network security Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Radhwan Hussein Abdulzhraa Al-Sagheer,
Department Of Computer Science,
Faculty Of Education For Girls,
University Of Kufa,
Najaf, Iraq.
Email: radhwan.hu@uokufa.edu.iq
1. INTRODUCTION
Day by day the dependency on the Internet has increased in our daily lives, mainly in many
important fields such as educational organizations, communication companies, government facilities,
banking, and e-commerce. This adds many difficulties in managing the web and utilizing the application, for
example, protecting the user data, integrity, privacy, and availability [1]. All these reasons changed
the consideration of attackers to thinking about financial advantages, the attackers utilize diverse malware to
accomplish their objectives. Among the different sorts of malware, Botnet is one of the most genuine ways of
doing the crime online on the web [2]. Therefore, financial benefits are the main aim of generating botnets by
the attacker [3]. McAfee's Threat Report for the first quarter of 2019 showed that the number of newly
discovered malware threats has achieved more than 60 million threats. The whole malware estimated to reach
more than 800million before the end of 2018 [4]. Moreover, the statistics revealed by CenturyLinkin the first
half of 2019 showed that the average number of threats amounted to 3.8 million unique threats per month,
and explained that the top five countries suspected for the movement of botnets attack are the United States,
Spain, India, Indonesia, and Turkey [5]. This huge number of malware threats caused by botnets have been
planned, each one becoming more resilient, unsafe, and smart. Fortunately, botnet detection methods have
also developed, which employ different approaches such as traffic analysis [6-8], DNS based methods [9]
and machine learning such as decision trees [10], Neural Network [11] and clustering [12].
The botnet detection modelin this study focuses on network traffic analysis under the behavior
characteristic that is flows generated by bots be different from normal flows. With this characteristic,
machine learning (ensemble classifier algorithms) can be attempted to classify flows depending on their
behavior with the possible highest accuracy. It is important to select the essential features by using some

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Int J Elec & Comp Eng, Vol. 10, No. 3, June 2020 : 2543 - 2550
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methods, such as information gain. The process of feature selection consequently guaranteed high accuracy
and reduced training time when performing, which is mentioned by [13]. The results of the detection methods
were verified using CTU-13 Dataset and 10 fold cross validation was adopted to evaluate the proposed
model performance.
2. RELATED WORKS
Recently, there has been growing attention in strategies for Botnet detection. Whereas it is important
to learn how a botnet has an infection on the PCs, it is more serious to determine the infested device prior to
it is exploited to set mischievous actions. There are various techniques have been introduced to detect
Botnets. These methods can be categorized into signature based, anomaly based, DNS based and data mining
techniques [14]. The signature based techniques, Behal [14] have proposed the “N-EDPS” which is
a signature based system for botnet detection and prevention. Through monitoring the outbound traffic, their
system concentrates on discovering and stopping malware infections especially botnets. They employed
the current freely available software which is open source usually. For detection, they utilized “BotHunter”
and “Snort Inline” for the prevention [6]. By using several network traffic anomalies, the anomaly basedhas
tried to identify Botnet. For example, high volumes of traffic, traffic passing to unusual ports, high network
latency and anomalous behavior may indicate the existence of bots in the network [15]. These trends can
detect new types of the bot. Karasaridis [7] have presented an approach to detect IRC botnet controllers from
Netflow. Their approach was able to detect the botnet communications which are encrypted. It can supply
extra BotHunter evidence-trails for infection actions [7].
Another method to detect Botnet has been developed by Wang and Paschalidis in 2017, their
proposed method has two phases, the first phase suggests two techniques in order to create the empirical
distribution. The two techniques are flow based approach and graph based approach. The flow based
approach is for approximating the histogram of quantized flows and the graph based approach for
approximating the grade distribution of node communication graphs. The second phase uses the social
network community to detect the Bots, this was done by a graph that captures the associations of connections
among nodes over time. They utilized real-world botnet traffic in the experiment which is CTU-13
dataset [8]. DNS-based detection techniques are utilized DNS-related network traffics generated by
the botnet. These techniques are similar to anomalous detection techniques where similar anomaly detection
algorithms are applied on DNS traffic. In 2019, Alieyan et al. proposed DNS rule-based detection technique
for botnet detection. They defined some rules to detect IPs that exhibition anomalies in DNS requests and
DNS replies. This rule technique is using to enable users to detect the existence of irregular behaviors of
DNS requests and DNS replies. These behaviors are proposed for the detection of any existence of DNS
based botnets and any source IP that shows such behaviours [9]. Mining based Detection techniques which
are considered as effective techniques for botnet detection. In 2013, Garg et al. presented a method for
the detection of P2P Botnets using several mining algorithms such as K-nearest neighbor, Naïve Bayes
and decision tree (J48).
The ability of these algorithms to detect P2P networks has been analyzed and compared by using
many of the features of network traffic [16]. K-medoids and K-means [12] are utilized to derive a set of rules
to decide which connections should be considered as a botnet. Datasets were extracted from the sources
ISOT and ISCX. Results on K-medoids were better for almost all these experiments than K-means.
As a methodology, Liao [17] used packet size to differentiate between P2P Botnet traffic and normal P2P
traffic. They provided the following observations. Initially, P2P Bots attempts to update information for other
Bots instead of remaining inactive. Next, the Bot mainly transfers data with lower communication rate.
In order to classify network traffic, three methods were used: Naïve Bayes, Bayesian networks, and J48.
However, the size of packets in P2P Botnet was found small compared with other P2P applications [17].
Others proposed neural networks-based botnet detection techniques to identify the legal and illegal patterns.
Through using some of the TCP-based features, a multi-layer neural network have been trained to detect
HTTP botnets. The results showed that this method is effective and can detect HTTP botnets at a low false
positive rate [18]. Graphical Turing tests "VISUALCOM", "IMGCOM", and "AD-IMGCOM" have been
used in building the model to prevent and detect the DDoS attacks in cloud computing from a botnet.
This model is implemented with a queuing model [19].
3. BOTNET OVERVIEW
Botnets are networks comprising of a huge number of PCs infected by Bots. These infected PCs,
remotely controlled by “botmasters” to implement specific malicious activities. The attacker arranges
a communication station to direct instructions to the Bots and to obtain results from them [20].

Int J Elec & Comp Eng ISSN: 2088-8708 
Botnet detection using ensemble classifiers of network flow (Zahraa M. Algelal)
2545
This communication channel is called the command and control (C&C) channel. TheC&C is the main feature
that distinguishes between Botnet and other types of malware [21]. Botnets may be categorized based on
the C&C mechanism into two major types: centralized and decentralized C&C [22]. The attacker or
botmaster is usually used the C&C server to direct a command to the bots in centralized botnets as illustrated
in Figure 1(a). Due to its uncomplicatedness, the centralized botnet is widely used via numerous botnet
groups. The IRC-based botnets and HTTP Botnet are considered among the most famous of botnet
approaches. However, the single point of failure C&C server in centralized Botnet is the major problem in it.
A shutdown of the C&C server might result in a lack of communication among the bots and
the botmaster [23]. The next generation of botnets, attackers have started to structure Botnets based on
a decentralized architecture, such as, the Peer-to-Peer botnet [24] which it adopted via many forms of
the botnet, for example, Waledac, Storm, and Conficker [25]. Peer-to-Peer botnet is a form that adopted
a decentralized architecture to avoid having any single point of failure. In P2Pbotnet as illustrated in
Figure 1(b), there is no central server, and bots are linked to each other topologically and act as a bot (client)
and C&C (server) at the same time. For this situation, the botmaster can direct instructions to the infected
peers to implement any order or requesting information at any time [26].
Figure 1. Structures of botent
4. ENSEMBLER CLASSIFIER FRAMEWORK
Ensemble method constructs a set of classifiers (base learners) from training data and combines
them to classify new data examples by taking a vote (typically by weighted or un-weighted) of their
decisions [27]. The main idea behind the ensemble learning is to employ several individual classifiers and
combine their predictions to obtain a classifier that can work better than each of them [28]. In this research,
the most three common ensemble approaches: Bagging, Boosting and random forest methods have been
used, as shown in Figure 2 [29].
4.1. Bagging
Bagging or bootstrap aggregating is a method to get multiple learners, where the training data set for
each learner is produced by random uniformly sampling with replacement from the original data set [30].
Bagging is consists of two parts: bootstrap and aggregation. A significant reduction in error could produce
when the combination of independent base learners happens, thus, it is essential to keep the base learner
independent as possible. The bootstrap distribution is utilized via the bagging technique to generate diverse
base learners. Using random sampling and replacement, the bagging method produces bootstrap sampling of
the training data, it implemented bootstrap sampling [31] to generate data subsets to train the base learners.
Moreover, several repeats of the original dataset are formed through utilizing random selection with
replacement. Next, every dataset is utilized to form a new learner and the formed set of learners is used to
construct an ensemble. For aggregating the outputs of the base learners, bagging utilizes one of the most
common methodologies for classification, which is voting while it uses an averaging approach to dealing
with the regression problem.

 ISSN: 2088-8708
2546
4.2. Boosting
Boosting technique also called ARCing “Adaptive Resampling and Combining” [28]. It is related to
the algorithms that can convert weak learners to strong learners. Generally, we can be defined as the weak
learner as the learner which is slightly better than the random guess. Oppositely, the strong learner is very
close to a perfect result. Boosting is a common method utilized to improve learning method performance.
The concept behind boosting is that a weak learner can be boosted to a strong learner Schapire [32] proposed
the boosting technique for that purpose. Boosting is consider as an advancing additive model and it utilizes
the whole dataset for each stage. This technique merges the outputs from various classifiers with the aim of
produce an effective classifier [33].
4.3. Random forest
The random forest belongs to the family of ensemble approaches. It grows many decision trees by
utilizing randomly partitioning the training data and features, where each tree is built depends on the values
of an independent set of random vectors of the training dataset. These random vectors produced from a fixed
probability distribution since the probability distribution is diverse to concentrate on instances, which has
difficulties to classify [34]. The randomization aids in reducing the correlation among decision trees to
improve the generalization error of the ensemble [30].
(a) (b) (c)
Figure 2. Shows the basic workflow for (a) Boosting, (b) Bagging, and (c) Random forest
5. PROPOSED MODEL
The proposed system for the Botnets detection, the classification of network traffic is achieved by
applying three different Ensemble classifier algorithms: Bagging, Boosting and Random Forest. The results
of the detection methods were verified using CTU-13 Dataset and 10 fold cross validation was adopted to
evaluate the proposed model performance. The framework of our system is described in Figure 3.
Figure 3. The proposed framework for botnet detection

2547
5.1. Dataset
The CTU-13 dataset [35] is one of the largest NetFlow datasets available that contains botnet
traffic as well as normal and background labeled data. These data were collected by the Czech Technical
University (CTU), 2011. The CTU-13 dataset has 13 datasets (called scenarios) of different botnet samples.
In addition to that, each of these scenarios has been recorded in a separate file as a NetFlow which using
CSV notation. These NetFlow files include the following attributes: Start Time, Duration, Source IP address,
Source Port, Direction, Destination IP address, Destination Port, Protocol State (e.g., UTP, TCP),
SToS (Type Of Service), Total Packets (exchanged between source and destination), Total Bytes, and Label
(e.g., background, normal, and botnet).
5.2. Feature selection
In the Botnet detection technique, one of the essential parts is feature extraction. By experimenting
not all features have similarly contributed to the result, some of them are significant and pertinent than
the other to the learning and analysis process. The redundancy of features may cause a reduction in
the accuracy, to rank the features in this paper, the information gain (1) measure has been used [36].
IG(A) = H(S) − ∑
St
St
H(Si) …
(1)
where H(S) is the entropy of the given a training set S and H(Si) is the the entropy of the ith subset of
the training set Since the attribute A is observed. The gained information is utilized to assist in ranking
the attribute in machine learning and the attribute with the high IG is ranked higher than the other attributes
because it has a stronger power in classifying the data. Figures 4 show that the classification of the (12)
attributes of the CTU-13 dataset sorted in descending order by information gain. After ranking the attributes
using information gain the best ones are selected Therefore the top 8 attributes based on their importance
value are considered in this work. The selected attributes are: < Source IP, Destination IP, Start Time,
duration, IP protocol, protocol state, the total number of packets and total bytes exchanged>.
Figure 4. The information gain for each attribute (A base-10 log scale is used for the Y axis)
5.3. Detection methods
The research introduces three Ensemble methods to identify between botnet and normal traffic by
classifying the corresponding flows. We have used bagging, AdaBoost, Random Forest method of
the ensemble-based classifier. The machine learning algorithms like JRip, Naïve Bayes and REPTree have
been deployed as a base classifier on ensemble methods.
 JRip: This class applies a rule-suggestion learner, “Repeated Incremental Pruning to Produce Error
Reduction” (RIPPER).
 Naïve Bayes: It depends on what is called the Bayesian theorem, It's particularly appropriate if the input
dimensions are high.
 REPTree: “Reduced Error Pruning Tree (REPT)” Builds a decision tree using information gain as
the partitioning criterion and prunes it using reduced error.

 ISSN: 2088-8708
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6. EXPERIMENTAL RESULTS
In our experiments, we have used CTU Botnet Dataset (Scenario 11), which already contains labeled
bidirectional net flows, The selected attributes by information gain are: Source IP, Destination IP, Start Time,
duration, IP protocol, protocol state, the total number of packets and total bytes exchanged, as shown in
Figure 4. A data mining software called WEKA has been used to apply ensemble algorithms to this dataset.
WEKA is a group of machine learning algorithms for solving data mining tasks. The algorithms can either
directly applied by using GUI or called from Java code. Because the size of the downloaded data is too large
to be processed by the available PC machines, so to deal with this problem a small part of the data was
randomly selected that can be handled by the available devices. This sample of data was entirely random
selected to guarantee that the results of the analysis stay unbiased by the selective process.
Five different measures were utilized to evaluate the performance of the proposed method, those
measures are Accuracy, False Positive Rate, Precision, Recall, and F-measure. The ten-fold cross-validation
technique was adopted to estimate the accuracy of the proposed method where the dataset is split at random
manner into similarly exclusive and equal-sized subsets. Also, the cross-validation method guarantees that
every part of the basic dataset is utilized in a similar number of times in training and testing. The generated
results usingensemble methods with the three different classification schemes (JRip, Naïve Bayes and
REPTree as a base classifier) are given in Table 1.
Table 1. Performance comparison table of classifiers
Methods Accuracy% FPR Precision Recall F-measure
AdaBoost JRip 99.84 0.002 0.998 0.998 0.998
Naïve Bayes 98.12 0.038 0.982 0.981 0.981
REPTree 85.48 0.307 0.88 0.855 0.841
Bagging JRip 99.84 0.002 0.998 0.998 0.998
Naïve Bayes 99.1 0.018 0.991 0.991 0.991
REPTree 85.48 0.307 0.88 0.855 0.841
Random Forest 95.11 0.103 0.954 0.951 0.95
Table 1 present the comparison of ensemble algorithms over the 10 fold cross-validation concerning
different comparison measures. JRip classifier achieves the highest classification accuracy (99.84%) in both
AdaBoost and Bagging compared with the accuracy of Naïve Bayes (98.12%) and REPTree (85.48%) in
AdaBoost and with the accuracy of Naïve Bayes (99.1%) and REPTree (85.48%) in Bagging. Furthermore,
Table 1 can conclude the JRip classifier gives the lower false positive rate (0.002) in both AdaBoost and
Bagging and the highest false positive rate from REPTree (0.307) and it has a low accuracy too. Random
Forest also achieves high detection accuracy (95.11%) and a low false positive rate (0.103). The Ensemble
with JRip Classifiers model has been compared with five different methods which are clustering, Neural
Network, Recurrent Neural Network [37, 38], K-medoids, K-means [12], Long Short-Term Memory
(LSTM) [11], anddecision trees [10]. The comparative of results in Table 2 show that our proposal Ensemble
with JRip Classifiers model achieves better detection accuracythan the existing systems for botnet detection.
Table 2. A comparison of the proposed model with other algorithms
Author Data set Methods Accuracy (%)
Bansal and Mahapatra[37] ISCX &
CTU-13
Clustering 98.39
Neural Network 89.38
Recurrent Neural Network 83.09
Alejandre et al. [12] ISOT&
ISCX.
K-medoids 69.99
and K-means 73.37
Sinha K. [11] CTU-13 Long Short-Term Memory (LSTM) 96.2%
Khan R. et al [10] ISOT &
CTU-13
decision trees 98.7%.
Proposed model
(Ensemble Classifiers)
CTU-13
(Scenario 11)
AdaBoost+ JRip
Bagging+ JRip
99.84
7. CONCLUSION
In this paper, we have presented an approach to deal with botnet detection problem, which is
considered as a serious and critical threat of internet security. One approach to handle this problem is by
recognizing botnet actions and infected devices to provide vital safety measures. The proposed model was

2549
based on “ensemble classifiers methods” which are performing better performance through combining
multiple algorithms in the process of botnet analysis. Also, through the feature selection process, the most
significant features were extracted for the analysis process to increase the accuracy and decrease the time as
well as resources. To evaluate this proposed methodology, we have performed experimental assessments on
the CTU botnet dataset and the performance of the proposed model was assessed utilizing 10 fold cross-
validation. The results showed that the proposed model was effective and has promising results.
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