Optimal remote access trojans detection based on network behavior

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 3, June 2019, pp. 2177~2184
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i3.pp2177-2184  2177
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Optimal remote access Trojans detection based
on network behavior
Khin Swe Yin, May Aye Khine
Faculty of Computing, University of Computer Studies, Myanmar
Article Info ABSTRACT
Article history:
Received Jan 15, 2018
Revised Dec 18, 2018
Accepted Jan 11, 2019
RAT is one of the most infected malware in the hyper-connected world. Data
is being leaked or disclosed every day because new remote access Trojans
are emerging and they are used to steal confidential data from target hosts.
Network behavior-based detection has been used to provide an effective
detection model for Remote Access Trojans. However, there is still short
comings: to detect as early as possible, some False Negative Rate and
accuracy that may vary depending on ratio of normal and malicious RAT
sessions. As typical network contains large amount of normal traffic and
small amount of malicious traffic, the detection model was built based on the
different ratio of normal and malicious sessions in previous works. At that
time false negative rate is less than 2%, and it varies depending on different
ratio of normal and malicious instances. An unbalanced dataset will bias the
prediction model towards the more common class. In this paper, each RAT is
run many times in order to capture variant behavior of a Remote Access
Trojan in the early stage, and balanced instances of normal applications and
Remote Access Trojans are used for detection model. Our approach achieves
99 % accuracy and 0.3% False Negative Rate by Random Forest Algorithm.
Keywords:
Network behavior detection
Random forests algorithm
Remote access Trojans
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Khin Swe Yin,
Faculty of Computing,
University of Computer Studies, Yangon,
No.4, Main Road, ShwePyiThar Township, Yangon, Myanmar.
Email: khinsweyin@ucsy.edu.mm
1. INTRODUCTION
An organization or a person suffers financial loss, reputation loss and business disruption because of
data breach. As the threat of data breaches is increasing every year, security of confidential information is
more important than before. One of the main reasons of occurring data breach is targeted malware attacks.
Remote Access Trojans are installed on endpoints using drive-by-download, email and USB tactics. If a
computer is infected with Remote Access Trojan, its’ command and control traffic stealthily control the
victim and steals confidential information. Advanced persistent threats gather confidential data from target
hosts by planting Trojans. Firewall, intrusion detection/prevention systems and antivirus scanners are used to
secure network from malicious activities. There are two detection techniques: host-based and network-based.
As host-based detection system has to be installed on each host [1], [2], it has some complexity and
overhead. Network based detection technique applies Deep Packet Inspection (DPI) techniques that gain a
very high accuracy but they cannot detect unknown Trojans. Deep packet inspection uses regular expression
(RE) matching [3].
It examines packet payload whether it is matched with any predefined regular expressions. The
attack patterns or signatures of antivirus scanner and intrusion detection systems are defined from known
malware. A signature is a sequence of bytes or sequence of events or sequence of system calls, etc [4], [5]. If
the attack signature is slightly changed by hackers using simple obfuscation technique such as inserting no-

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Int J Elec & Comp Eng, Vol. 9, No. 3, June 2019 : 2177 - 2184
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ops and code re-ordering or a novel attack appears, the unseen attack will be considered as acceptable pattern
and this attack will be missed. Moreover, maintenance of the signature database is an extremely tedious and
time-consuming.
Although various techniques have been introduced to detect remote Access Trojans, there remains
two challenges: (1) there is weakness for extracting correct information for features in the early stage and (2)
false negative rate that should be taken care of, (3) overhead. When to stop and cut the traffic is very
important to extract effective features. Since some features are extracted from a session that starts from SYN
packet in TCP three -way handshake to FIN/ACK packet and some are obtained from a session that starts a
connection to the end of the traffic, time takes long and confidential information will be leaked before
detection. Network behavioral analysis has been used to classify network traffic applications and to detect
malware [6], [7]. As features are extracted from the early stage that depends on packet interval time, correct
information for features cannot be obtained when error-recovery feature like TCP retremission occurs.
Antivirus scanners needs to be installed on each host and it needs to be updated daily. Moreover, just a
simple ratio of malicious and normal applications is applied for building a model. As typical network
contains approximately 99.99% of normal instances and small number of malicious instances, just a simple
ratio of normal and malicious traffic instances is not enough to approach a best detection model with
effective features and no overhead.
In this paper features are extracted within the first twenty packets that starts SYN of TCP three-way
handshake to twentieth packets without depending on how long packet interval time takes. First twenty
packets are enough to detect malicious traffic of remote access trojans in the early stage, and it can avoid
error-recovery features too. In addition, RATs are run many times and their different behaviors are captured.
Different ratios of normal and malicious instances are applied for analyzing detection model. Our approach
reduces FNR to 0% while maintaining early stage detection. Moreover, it is easy to manage through network
and we do not need to install and update application to each terminal as it is network-based approach.
As a behavior-based detection, both unknown RATs and variants of known RATs can be detected without
time consuming. The paper is organized as follows: literature review is summarized in Section 2, research
method is presented in Section 3, Section 4 describes results and discussion, and the paper is concluded in
Section 5.
2. LITREATURE REVIEW
Several techniques have been used to detect variants of malware. Features are categorized on the
basis of static and dynamic analysis of program files [8]. In static analysis, the behavior of program is
observed by analyzing its binary code or internal structure of files without actually executing it [9]. It is
vulnerable to code obfuscation techniques. Dynamic analysis is performed by running a program. In dynamic
analysis behavior of malware is monitored in emulated environment. It can deal with code evasion
techniques [10].
Network behavioral analysis has been done in recent years for detecting malware. But behavioral
features are different depending on when the traffic is cut or stopped to extract features. [11] uses flow level-
based features and IP level-based features in order to describe Trojan network behavior accurately. At the
flow level, two features – (1) duration, and (2) packet time interval are extracted. At the IP-level, 4 features-
(1) number of inbound/outbound packets, (2) volume of inbound/outbound traffic, (3) duration of the
communication session, and (4) number of transport layer connections. These features are extracted from the
session from a SYN packet in the TCP three-way handshake and ends with a FIN/RST packet. So, it takes
time, and confidential information may be leaked before detection. The accuracy is over 91% and FPR is less
than 3.2%.
Five typical characteristics are used to describe malicious behavior of RATs in [12]. They are (1)
ratio of send and received traffic size, (2) number of connections, (3) proportion of upload connection,
(4) proportion of concurrent connection, (5) number of distinct IPs. Its’ accurate detection is 97.05% and
FPR is 2.94%. As its features are extracted from the start of an application’s connection to the end, it may be
impossible to detect RAT as fast as possible. The exact number of normal and malicious instances is not
mentioned in these works. The malicious traffic of RATs can be detected in the early stage of TCP
communication [13]. The early stage of a session is a packet list that starts from the SYN packet of TCP
three-way handshake and ends until each packet interval time is less than the threshold t seconds. It does not
take into consideration of TCP’s error recovery features like TCP Retransmission that occurs in the stage of
TCP handshaking. Total 175 sessions are used for classifying. 165 are normal sessions and 10 sessions are
RATs’. The detection accuracy is over 96% and FNR is 10% by Random Forest algorithm.
The behavior features are mainly distributed in the network layer, transport layer and application
layer [14]. A closed-loop feedback model is designed to remove some false alarms from the detected results.

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The false positive results are fed back as inputs for the training sets of machine learning model. So, it takes
time and much overhead. 224 data flow of Trojan traffic and 276 data flow of normal applications are used
in [14]. Its detection rate is 97.7%. The unbalanced ratio of normal and malicious sessions is used
in [13]-[15]. RATs are run once and captured the traffic to extract features.
A hybrid approach that combines anomaly, behavior and signature-based detection techniques is
designed to detect zero-day attacks including worm, virus, and so on [16]. So, it is computationally
expensive. Since it uses features like same_srv_rate that is percentage of connections to the same service in
count feature, Pkt_count_legitimate_ports that is among the past 100 connections whose destination port is
same to the port in the legitimate ports list, it needs time and it is not possible to detect the attacks as early as
possible. The first few packets are used for early traffic classification, but it needs to use new appropriate
features [17]. The summary of related works is shown in Table 1.
Table 1. Summary of Related Works
Related
Works
Approach Limitation
[8]-
[10]
Reviewed malware detection
Signature based and behavior based detection
Static and dynamic analysis
Signature based approach cannot detect
unknown malware
Much false positive rate in behavior
based detection
[11]
Network behavior based technique.
It uses flow-level and IP-level features, e.g. duration, transport layer
connection
It takes 5 minutes to terminate sessions
in this work
RATs will be detected after they stay
long in the victim machine
[12]
Application network behavior based approach
Examples of features- number of connection, proportion upload of
connection,
Complex to obtain features and to
manage
It may detect RATs after their long stay
in the victim host
[13]
It detects RATs in the early stage of network traffic
Early stage is defined depending on packet interval time
A simple ratio of RATs and normal applications is used (10 instances of
RATs and 175 instances of normal applications)
It does not consider error recovery
features- TCP retransmission- and
RATs will be missed
Just a simple ratio of instances is not
enough to obtain best detection model
[14]
Features are distributed in three layers: network layer, transport layer and
application layer
e.g. many sub-connections during primary connection, communications time
False Positive Rate is fed back as input to adjust the training model
224 instances of Trojans and 276 instances of normal applications are applied
Complex to extract and obtain features
Overhead
Just a simple ratio of instances is used
for detection model
[16] A hybrid approach for zero days attacks It may not be early stage detection
[17] Early traffic clcassification It needs effective features
The ability of a host to retransmit packets is one of TCP’s most fundamental error-recovery features
in Wireshark. When a packet is sent, but the recipient has not sent a TCP ACK packet back, the transmitting
host assumes that the original packet was lost and retransmits the original packet [18]. It is called TCP
Retransmission. If TCP retransmission occurs, packet interval time takes more time than usual. Moreover,
this kind of situation often occurs in the steps of TCP three-way handshake before establishing connection for
transferring data. It is often found in the traffic of remote access Trojans, and if the stopping way for
extracting features is packet interval time, correct information for features cannot be obtained in this state.
The previous works do not take into consideration this situation and some use packet interval time for
defining the early stage and extracting features. In this paper, each RAT is run many times and different
behavior of RATs during the first twenty packets are captured and, both balanced and unbalanced sessions
are used for building a detection model in order to detect RATs in the early stage and to avoid the bias
problem of unbalanced dataset.
3. RESEARCH METHOD
Our method consists of three main phases: Feature Extraction, Training and Detection. After
collecting network traffic, features are extracted for each session and labelled, and then these sessions are
trained with three supervised machine learning algorithms. Then, the detection model is obtained and used to
test with a real session. Our work mainly focuses on Feature Extraction.

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3.1. Pre processing
Wireshark is used to capture network traffic traces. The network traces are filtered by TCP protocol.
Two different IP addresses that there is interaction between them are chosen and the traces are cut for the
first twenty packets that starts from SYN of TCP three-way handshake to the twentieth packet. Then the
traces are divided into sessions, and then sessions are labelled.
3.2. Feature extraction
How to extract features for the first twenty packets is shown in Figure 1. Firstly, basic 10 features
are initialized, and then the values of basic features are collected until the number of packets is equal to 20.
Next, 4 features are calculated depending on the value of the basic features. Finally, 7 features are chosen to
generate a feature vector for a session. The selected 7 features are described in detail in Table 2. Comparison
of features is shown in Table 3.
Figure 1. Process of feature extraction
Table 2. Selected Features
No Feature Description
1 Outbyte Outbound data byte
2 Inbyte Inbound data byte
3 InByteByInPac rate of Inbound data Byte/ Inbound number of packets
4 OutByteByOutPac rate of Outbound data byte/Outbound number of packets
5 Duration duration from the first packet to twentieth packets
6 OutByteByInByte ratio of Outbound data byte/Inbound data byte
7 OutPacByInPac ratio of outbound number of packets/ inbound number of packets
Yes
Initialization of features
(PacNum=OutByte=OutPac=InByte=InPac=Dur=0),
(OutPacByInPac=OutByteByOutPac=
InByteByInPac=OutByteByInByte=NULL)
( (PacNum ≤ 20)
Read a traffic packet
No
(1) Increase packet number(PacNum)
(2) Increase outbound data byte(OutByte)
(3) Increase outbound packet number(OutPac)
(4) Increase inbound data byte(InByte)
(5) Increase inbound packet number(InPac)
(6) Increase time (Dur)
(1) Calculate Inbound data/ inbound number of packets (InByteByInPac)
(2) Calculate outbound data/outbound number of packets
(OutByteByOutPac)
(3) Calculate outbound data/inbound data (OutByteByInByte)
(4) Calculate outbound number of packets/ inbound number of packets
(OutPacByInPac)
Selected features (OutByte, InByte, Dur,
InByteByInPac, OutByteByOutPac, OutByteByInByte,
OutPacByInPac,)

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Table 3. Comparison of the Selected Features in Early 20 Packets
Features Type Trend
OutByte
N 91.33% are more than 500 bytes
R 67% are more than 500 bytes
InByte
N 99.667 % are more than 200 bytes
R 11% are more than 200 bytes
InByteByInPac
N 99.667 % are more than 20 bytes
R 10.333% are more than 20 bytes
OutByteByOutPac
N 87.333% are more than 50 bytes
R 67.333% are more than 50 bytes
Duration
N 33.33% take more than 10 seconds
R 73.667% take more than 10 seconds
OutByteByInByte
N 28% are more than 1
R 100 % are more than 1
OutPacByInPac
N 33.333% are more than 1
R 28.667 % are more than 1
N: Normal Application, R: RAT
3.3. Learning with machine learning algorithms
Weka, datamining tool is used to load datasets and three machine learning algorithms are used for
building detection model. Three machine learning algorithms used in the experiment are Decision Trees
(DT), Random Forests (RF) and Naïve Bayes (NB).
3.3.1. Decision trees
In decision trees, the process is broken down into individual tests which begin at the root node and
traverse the tree, depending on the result of the test in that particular node. The tree begins at the root node.
From the root node the tree branches or forks out to internal nodes. The decision to split is made by impurity
measures [19].
3.3.2. Random forest
Random forest is an ensemble classifier that consists of many decision trees and outputs the class
that is the mode of the class's output by individual trees. The method combines Breiman's "bagging" idea and
the random selection of features and it improves prediction accuracy [20].
3.3.3. Naïve bayes
Naive Bayes is a widely used classification method based on Bayes theory. Based on class
conditional density estimation and class prior probability, the posterior class probability of a test data point
can be derived and the test data will be assigned to the class with the maximum posterior class probability
[21]. Calculating the conditional probability as follows:
𝑃(ℎ 𝐷⁄ ) =
𝑃(𝐷 ℎ⁄ )𝑃(ℎ)
𝑃(𝐷)
(1)
3.4. Evaluation
k-fold Cross Validation is used to validate the result of classification in the experiment. Accuracy,
False Negative Rate (FNR) and False Positive Rate (FPR) are used for evaluation. Accuracy gives the
correctly classified number of both normal and malicious instances on total instances. FPR expresses that the
incorrectly classified number of normal instances on the total normal instances. FNR shows that the
incorrectly classified number of malicious RAT instances on the total RAT instances. The less FNR while
maintaining high accuracy, the better the detection system is for not missing malicious sessions. How to
calculate Accuracy, FPR and FNR is shown below:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑜𝑟𝑚𝑎𝑙 𝑎𝑛𝑑 𝑅𝐴𝑇 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑜𝑟𝑚𝑎𝑙 𝑎𝑛𝑑 𝑅𝐴𝑇 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
(2)
𝐹𝑁𝑅 =
𝐼𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝐴𝑇 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓 𝑅𝐴𝑇 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
(3)
𝐹𝑃𝑅 =
𝐼𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑁𝑜𝑟𝑚𝑎𝑙 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑁𝑜𝑟𝑚𝑎𝑙 𝐼𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠
(4)

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4. RESULTS AND DISCUSSION
A virtual environment that attackers and victims are running is set up. The attacker is a place where
RAT is executed, and the victim is a place where the attacker’s server.exe is executed. 10 types of Remote
Access Trojans and 10 normal applications are used in the experiment. Wireshark is run on the victim to
capture and collect network traffic. The most widely used RATs are applied in the experiment. Normal
applications include cloud services, p2p download tools, browsers and social services that most of people use
in Internet today. RATs and normal applications used in the experiment are shown in Table 4.
Table 4. RATs and Normal Applications used in the Experiment
No RATs Normal applications
1 ImminentMonitor Dropbox
2 KilerRat Pcloud
3 NjRat Skype
4 Cerberus YahooMessenger
5 Xtreme Facebook
6 Pandora Bittorrent
7 CyberGate BitComet
8 SpyGate Google
9 Xena Firefox
10 Babylon Chrome
Network behavior features for a session are extracted from the trace that starts from a SYN packet in
the TCP three-way handshake and ends at twentieth packets. It is the very first time traffic that collects after
the victim is infected by RAT. If this RAT is not detected and removed from victim’s computer, it always
connects back to the attacker. It is a considerable situation because the attacker may stay as long as possible
to control the victim. When a RAT is run next time after system reboots, it always sends connection back to
the attacker. In our experiment, each RAT is run many times in order to capture the variant behavior of RAT.
In this way the number of malicious sessions is also increased without using sampling method and the
balanced normal and malicious sessions are obtained for building a detection model. Different ratios of
normal and malicious instances, and their results are shown in Table 5.
Table 5. Results of Naïve Bayes (NB), Decision Trees (DT), Random Forests (RF)
Ratio of normal and
malicious instances
NB DT RF
Acc FNR FPR Acc FNR FPR Acc FNR FPR
N150-RAT10 0.963 0.6 0 0.981 0.2 0.007 0.988 0.1 0.007
N300-RAT10 0.971 0.7 0.007 0.99 0.2 0.003 0.99 0.2 0.003
N300-RAT300 0.878 0.217 0.027 0.992 0.003 0.013 0.993 0.003 0.01
Acc: Accuracy, FNR: False Negative Rate, FPR: False Positive Rate
The classification methods are Naïve Bayes, Decision Trees and Random Forests. 15 sessions from
each normal application and 1 session from each RAT are collected and, 150 normal instances and 10 RAT
instances are used for building a model. Next, 300 normal sessions and 10 RATs sessions are applied for
detection model. Then, 300 normal sessions from 10 normal applications and 300 RAT sessions from 10
RATs are applied to build a model.
In Naïve Bayes, accuracy is high but FNR is 0.6 and 0.7 while using unbalanced ratios. Although
it’s FNR reduces to 0.217 with balanced instances, its accuracy decreases to 0.878. DT has a slight difference
in accuracy although different ratios of instances are used for classification. DT has FNR -0.2 when
unbalanced ratios of instances are used. But its FNR is reduced to 0.003 when balanced instances are
classified. The accuracy of RF does not change much with both balanced and unbalanced instances. The
False Negative Rate of RF is fluctuared. It is 0.1 when 150 normal and 10 RAT instances are classified. It
increases to 0.2 with 300 normal and 10 malicious instances. But it decreases to 0.003 when the balanced
instances are used.
Among three algorithms- Naïve bayes, Decision Trees and Random Forests- DT and RF maintain
high accuracy for different ratios of instances. FNR and FPR of DT and RF are the least among these
algorithms. RF is slightly better than DT. They are suitable algorithms for detecting RATs. They can reduce
FNR to 0.003 while maintaining high accuracy 0.99 when they use balanced ratio of normal instances and
malicious instances. So optimal detection model with best accuracy, least FNR and least FPR is obtained by
Random Forest algorithm.

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A performance comparison of two approaches is shown in Table 6. The first approach depends on
packet interval time [13] and the second one is first twenty packets which is our proposed approach.
300 normal instances and 10 RATs instances are classified by random forest. The detection that depends on
packet interval time gets 97% accuracy and 0.6 FNR. The detection during first twenty packets obtains 99%
accuracy and 0.2 FNR.
Table 6. A Performance Comparison of Two Detection Approaches
Detection Approaches
Random Forest
Accurcy FNR FPR
Early stage detection that depends on
packet interval time
0.977 0.6 0.003
Detection during first twenty packets 0.99 0.2 0.003
5. CONCLUSION
In this paper, the idea of extracting network behavioral features in the early twenty packets for
detecting Remote Access Trojans is proposed and implemented. The behavior of Remote Access Trojans is
different from normal applications in the early twenty packets of network traffic. Different ratios of normal
and malicious instances are used for building detection model. One session for each RAT is not enough to
build a best model, and running RAT many times is the best for describing variant behaviors of RATs and
increasing malicious instances in order to build optimal detection model. Random Forest algorithm is the best
detection model since its accuracy is 99.3%, its FNR is 0.003 and its FPR is 0.01. Thus, it helps to reduce
data leakage and increase the security of confidential information. This approach relies on the network
behavior features that uses TCP protocol. Future work will be to increase the number of RAT samples and
normal applications in order to achieve comparable classification accuracy, FPR and FNR for detection
system of RATs in production environments with effective feature set and no overhead.
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[20] L. E. O. Breiman, “Random forests,” Machine Learning, vol/issue: 45(1), pp. 5-32, 2001.
[21] T. M. Mitchell, “Machine Learning, Bayesian Learning,” pp. 154-178, 1997.
BIOGRAPHIES OF AUTHORS
Khin Swe Yin received M.C.Sc in Computer Science from University of Computer Studies,
Mandalay(UCSM) in 2009. She is a PhD candidate in University of Computer, Studies,
Yangon(UCSY). Her research interest includes Network Security and Machine Learning.
May Aye Khine received M.I.Sc and Ph.D. degrees in information technology from the University
of Computer Studies, Yangon, Myanmar in 1999 and 2004, respectively. She is currently a full
professor from faculty of computing in the University of Computer Studies, Yangon, Myanmar.
Her main research interests include Big Data Analytics, Mathematical Modeling especially in
Computational Methods and Data Science.

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