A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 3, June 2020, pp. 2701~2709
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i3.pp2701-2709  2701
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
A technical review and comparative analysis of machine
learning techniques for intrusion detection systems in MANET
Safaa Laqtib1
, Khalid El Yassini2
, Moulay Lahcen Hasnaoui3
1,2
Informatics and Applications Laboratory (IA), Department of Mathematics and Computer Science,
Faculty of Sciences, Moulay Ismail University, Morocco
3
ISIC ESTM, L2MI Laboratory, ENSAM, Moulay Ismail University, Morocco
Article Info ABSTRACT
Article history:
Received Jun 28, 2019
Revised Nov 20, 2019
Accepted Dec 4, 2019
Machine learning techniques are being widely used to develop an intrusion
detection system (IDS) for detecting and classifying cyber attacks at
the network-level and the host-level in a timely and automatic manner.
However, Traditional Intrusion Detection Systems (IDS), based on
traditional machine learning methods, lacks reliability and accuracy. Instead
of the traditional machine learning used in previous researches, we think
deep learning has the potential to perform better in extracting features of
massive data considering the massive cyber traffic in real life. Generally
Mobile Ad Hoc Networks have given the low physical security for mobile
devices, because of the properties such as node mobility, lack of centralized
management and limited bandwidth. To tackle these security issues,
traditional cryptography schemes can-not completely safeguard MANETs in
terms of novel threats and vulnerabilities, thus by applying Deep learning
methods techniques in IDS are capable of adapting the dynamic
environments of MANETs and enables the system to make decisions on
intrusion while continuing to learn about their mobile environment. An IDS
in MANET is a sensoring mechanism that monitors nodes and network
activities in order to detect malicious actions and malicious attempt
performed by Intruders. Recently, multiple deep learning approaches have
been proposed to enhance the performance of intrusion detection system.
In this paper, we made a systematic comparison of three models, Inceprtion
architecture convolutional neural network (Inception-CNN), Bidirectional
long short-term memory (BLSTM) and deep belief network (DBN) on
the deep learning-based intrusion detection systems, using the NSL-KDD
dataset containing information about intrusion and regular network
connections, the goal is to provide basic guidance on the choice of deep
learning models in MANET.
Keywords:
Attack
BLSTM
DBN
Deep learning
Intrusion detection system IDS
Inception-CNN
MANET
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Laqtib Safaa,
Informatics and Applications Laboratory (IA),
Department of Mathematics and Computer Science, Faculty of Sciences,
Moulay Ismail University,
Meknes, Morocco.
Email: laq.safaa@gmail.com
1. INTRODUCTION
A Mobile ad hoc Network (MANET) is generally defined as a network that has many free or
autonomous nodes [1], often composed of mobile devices or other mobile nodes that can arrange themselves
in various ways and operate without strict top-down network administration. There are many different types
of setups that could be called MANET and the potential for this sort of network is still being studied. Inherent
vulnerability of Mobile ad hoc Networks introduces new security problems, which mostly address

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 3, June 2020 : 2701 - 2709
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the network and data link layer of the protocol stack. Because each packet must be passed through
intermediate nodes quickly, that packet has to travel from the source to the destination. Malicious routing
attacks may target the routing detection or maintenance process by failing to follow the specifications of
the routing protocol [2, 3]. This increases the possibility of attacks such as eavesdropping, spoofing, denial of
service, and impersonation. Compared to fixed networks, Mobile ad hoc Network security is taken into
account from various points such as availability, privacy, reliability, encryption, authentication, access
control, and usage control. Due to the prominent characteristics of Mobile ad hoc Networks, security methods
used to secure fixed networks are not feasible for MANET [4]. New threats such as attacks from internal
malicious nodes, Byzantine, and wormhole attacks are difficult to defend. An Intrusion Detection System
(IDS) is an effective way to identify when an attack occurs in a MANET.
For the above reasons, it is very important to deploy in MANET as a second line of defense an
intrusion detection system [5]. Intrusion detection systems (IDS) are a mechanism for monitoring and
investigating events occurring in a computer system. An IDS incorporates methods for modeling and
discovering abnormal behaviors and complex techniques. They try to determine whether or not the network is
going through any malicious activity. This is typically accomplished by gathering data automatically from
a variety of systems and network sources and then analyzing the information for potential security issues.
Current intrusion detection and prevention methods, such as firewalls, access control protocols and
authentication, have several drawbacks in defending networks and devices from ever more advanced attacks,
such as a denial of service [6]. However, most systems based on such techniques are suffering from high
false positive and false negative detection rates and lack of continuous adaptation to evolving malicious
behaviors. Deep learning, therefore, allows to quickly perform data analysis and visualization, the aim is to
enable the detection of device vulnerabilities and flaws by security professionals. Several Deep Learning
(DL) approaches have been applied to the issue of intrusion detection to increase detection rates and
adaptability. Such techniques are often used to establish the attacks existing and detailed knowledge base.
The remainder of this paper is organized as follows. Section 2 is dedicated to discuss Security
attacks in MANET. Then, Section 3 describes the intrusion detection system architectures in MANET.
Section 4 provides a deep learning models for intrusion detection system in MANET. In section 5
the experiments and resultas. In section 6 we conclude by conclusion.
2. SECURITY ATTACKS IN MANET
Compared to wired infrastructure networks, Mobile ad hoc Networks (MANET) [7] are more
vulnerable to attacks. MANET face more security threats than centralized networks due to their dynamic
topology and the lack of centralized network administration. In the Mobile ad hoc networks, several
characteristics could be used to classify attacks. Examples would include looking at the behavior of
the attacks (passive vs. active), the source of the attacks (external vs. internal).
In active attack, the attacker is actively involved in the network operations and tries to change
the messages being transmitted. By disrupting the entire network process, the attacker may modify, insert,
forge, drop data. The frequency of this attack is high because the whole network can be brought down [8].
They are easy to detect as the network performance degrades significantly. In passive attack, the attacker
does not corrupt the shared data but listens to it. They are attempting to gain confidential information and
analyze the traffic patterns transmitted. They are difficult to detect because they do not interrupt or modify
the information sent or received. It is also possible to classify the attacks into two categories depending on
the domain of the attacks, namely external attacks, and internal attacks. Internal attacks are carried out by
nodes that are not part of the domain of the network. External attacks are triggered by nodes that are already
part of the network. External attacks are more severe than internal attacks [9].
2.1. Denial of service attacks (DoS)
A denial of service attack is an attempt to make a machine or network resource unavailable to its
intended users [10, 11], such as to temporarily or indefinitely interrupt or suspend services of a host
connected to the Internet. This attack can be launched at different layers. the physical layer, network layer,
transport layer.
2.2. Remote to user attacks (R2L)
Occurs when an attacker does not have an account on the victim machine and attempts to gain
access by sending packets to a machine over a network in order to generate some vulnerability on that
machine that allows him/ her to gain local access as a user of that machine.

Int J Elec & Comp Eng ISSN: 2088-8708 
A technical review and comparative analysis of machine learning techniques... (Safaa Laqtib)
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2.3. User to root attacks (U2R)
This attack occurs when normal system user illegally gains access to either root’s or super user’s
privileges such as Perl, xterm [12].
2.4. Probing
Probing occurs when an attacker scans a network in order to gather information or find known
vulnerabilities that allow him /her to hack the entire network. Usually, this method is used in information
mining such as saint, port sweep, Mscan, Nmap, etc [13].
3. INTRUSION DETECTION SYSTEM ARCHITECTURES IN MANET
Many intrusion detection systems (IDS) have been developed for MANET to detect various types of
attacks, IDS plays an important role in MANET to detect any type of attacks [14, 15]. An IDS is a software
system used to analyzes misbehavior and violation of policy, and then generate a report based on it,
Basically, intrusion detection system is classified into following three basic categories according to their
operational structure.
3.1. The standalone architecture
In this system, to determine intrusion, the intrusion detection system runs independently on
the individual node. All decisions made about a particular activity depend solely on information collected at
its own node, as there is no collaboration between nodes in the network [16]. Therefore, there is no transfer
of information. Even, as no alert information is transferred, a node in the same network does not have any
information about the other nodes in the network. Because of its limitations, this model is not efficient, it can
be used effectively in a network where all nodes already have an IDS installed. Compared to multi-layered
network infrastructure, this system is also suitable for single layer network. Since the information available
on any single node is not sufficient to detect intrusions, this system has not been chosen as MANET IDS.
3.2. The distributed and collaborative architecture
In this architecture, the intrusion detection engine is installed on each node in this architecture,
which monitors local audit data and detects intrusion We also participate in the cooperative detection and/or
response process by sharing audit information and/or detection results with neighboring nodes to solve
the problem. When the intrusion is captured, either a local response (e.g. alerting the local user) or a global
response may be issued by an IDS agent. Each node is involved in the method and response of intrusion
detection as having an IDS agent running on it [17]. An IDS agent is responsible for detecting and collecting
local information and data in order to identify any attack if an attack occurs in the network, as well as taking
an independent response. However, when the evidence is non-conclusive, neighboring IDS agents also
cooperate in global intrusion detection. This system, like standalone IDS, is also more suitable for flat
network systems, not multi-layer systems.
3.3. The hierarchical IDS intrusion detection architecture
Hierarchical IDS system Expand the distributed and cooperative IDS system functions and have been
implemented for multi-layer network infrastructures where the network is divided into various small
networks known as clusters. Usually, each cluster head has more functionality than other cluster members,
such as transmitting data packets to other clusters. We can therefore say that these cluster heads work in
some way as central points similar to wired network control devices such as routers, switches or gateways.
The multi- layering concept applies to intrusion detection systems where there is a proposal for hierarchical
IDS. Each IDS agent runs on a specific member node and is responsible for its node, i.e. monitoring and
deciding on intrusions detected locally. A cluster head is responsible for their node locally as well as globally
for their cluster, such as monitoring network traffic and announcing a global response when detecting
network intrusion [18].
4. DEEP LEARNING MODELS FOR INTRUSION DETECTION SYSTEM IN MANET
Deep learning is a class of Machine learning algorithms that uses multiple layers to progressively
extract higher level features from the raw input. The aim is to make machines like computers think and
understand how humans think by imitating the grid of the human brain connection, Deep learning
architectures such as deep neural networks, deep belief networks, recurrent neural networks and
convolutional neural networks have been applied to fields including computer vision, speech recognition,
natural language processing, audio recognition, social network filtering, machine translation, bioinformatics,

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drug design, medical image analysis, material inspection and board game programs, where they have
produced results comparable to and in some cases superior to human experts [19]. Supervised learning
algorithm is applied to a dataset that has features and each of those features associated with a label. However,
deep learning algorithms comes under unsupervised learning algorithms which are applied to a dataset which
has many features in order to learn useful properties from the structure of the dataset [20].
The Security applications of deep learning models like Intrusion Detection System (IDS), malware
detection, spam-filtering have become essentials in designing tasks for data protection, classification, and
prediction. These different types of tasks depending on the intelligence to build a model that usually classifies
and discriminates between samples of "benign" and "malign," such as attacks and benign packets [21].
The complexity of attack techniques tools is increased with the rapid increase with the use of Deep learning
models.There are lots of popular variants of Deep learning models like CNNs and RNNs. To solve
the hardness of training, BLSTM is proposed to alleviate some limitations of the basic RNN, Inception
convolutional neural network (CNN) which a variant of basic CNN and deep belief network (DBN).
This section gives a brief introduction of the models we have used in our experiments: the inception
architecture CNN, BLSTM and DBN [22].
4.1. The inception architecture CNN
Szegedy et al [23] suggest the Inception architecture CNN to solve the problem of a large number of
parameters and speed up the learning of CNN. An Inception network is typically a network consisting of
modules of the above type stacked on each other, with occasional max-pooling layers with phase two to halve
grid resolution. It seemed useful to start using Inception modules only at higher layers for technical reasons
(memory capacity during training) while retaining the lower layers in traditional convolutional fashion.
As we can see in Figure 1, the 1x1 convolutions are used to compute reductions before the expensive
3x3 and 5x5 convolutions. Besides being used as reductions, they also include the use of rectified linear
activation which makes them dual-purpose [24]. One of the main benefits of this architecture is that it makes
it possible to dramatically increase the number of units at each stage without an uncontrolled blow-up in
computational complexity. Another practically useful aspect of this design is that it is in line with
the principle that visual information should be processed on different scales and then aggregated so that
the next stage can simultaneously abstract features from different scales. The improved use of computational
resources allows for increasing both the width of each stage as well as the number of stages without getting
into computational difficulties. Another way to utilize the inception architecture is to create slightly inferior,
but computationally cheaper versions of it.
Figure 1. Architecture of the inception module with dimentions reductions [25]
4.2. Bidirectional long short-term memory (BLSTM)
Instead of running an RNN only in the forward mode starting from the first symbol, we start another
one from the last symbol running from back to front. Bidirectional recurrent neural networks introduce
a hidden layer to more robust processes by passing information in a reverse direction. Figure 2 illustrates
the architecture of a bidirectional recurrent neural network. In fact, this is not too dissimilar to the forward
and backward recursion we encountered above. The main distinction is that in the previous case these
equations had a specific statistical meaning. Now they are devoid of such easily accessible interpretaton and

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we can just treat them as generic functions. This transition epitomizes many of the principles guiding
the design of modern deep networks: first, use the type of functional dependencies of classical statistical
models, and then use the models in a generic form [26].
Figure 2. Architecture of BLSTM [27]
4.3. Deep belief network (DBN)
To overcome the overfitting problem in Multi-Layer Perceptron (MLP), we can set up a DBN,
do unsupervised pretraining to get a decent set of feature representations for the inputs, then finetune on
the training set to actually get predictions from the network. While weights of an MLP are initialized
randomly, a DBN uses a greedy layer-by-layer pretraining algorithm to initialize the network weights through
probabilistic generative models composed of a visible layer and multiple layers of stochastic, latent variables,
which are called hidden units or feature detectors. Restricted Boltzmann Machines (RBM) in the DBN are
stacked, forming an undirected probabilistic graphical model similar to Markov Random Fields (MRF):
the two layers are composed of visible neurons and then hidden neurons. The top two layers in a stacked
RBM have undirected, symmetric connections between them and form an associative memory, whereas lower
layers receive top-down, directed connections from the layer above. A hybrid model is established by
stacking up RBMs, as illustrated in Figure 3. The top two layers form the RBM and the lower layers form
a directed belief net [28]. This hybrid model is called a deep belief network (DBN). The deep-belief-network
is a simple, clean, fast Python implementation of deep belief networks based on binary Restricted Boltzmann
Machines (RBM). In our case, it was based on NumPy and TensorFlow libraries to take advantage of GPU
computation.
Figure 3. Hybrid model of the DBN after greedy layer-wise learning. The top two layers form the RBM and
the bottom layers form a directed belief network [29]

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5. EXPERIMENT AND RESULTS
5.1. Data preprocessing
The methodology discussed in this paper is applied on the entire NSL-KDD dataset. The NSL-KDD
dataset was proposed to deal with inherent problems of the KDD Cup 1999 dataset which contain too many
redundant records. An example of dataset record is ‘0 tcp ftp_data SF 491 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2
0 0 0 0 1 0 0 150 25 0.17 0.03 0.17 0 0 0 0.05 0 normal’. As you can see, data contains some text values
also. Pre-processing of original NSL-KDD dataset is necessary to make it a suitable input for learning
models. We need to transform the nominal features to numeric values. Only column number
2(Protocol_type), 3(Services), 4(Flag) and 42(Attack or Normal) contains nominal values [30].
5.2. Evaluation metrics
For evaluation purposes, Accuracy (ACC), Precision (P), Recall (R) metrics are used. These metrics
are calculated by using four different measures, true positive (TP), true negative (TN), false positive (FP)
and false negative (FN). Accuracy is the percentage of the records number classified correctly over total
the records. Precision means the percentage of your results which are relevant. On the other hand, recall
refers to the percentage of total relevant results correctly classified by your algorithm [31].
True Positive Rate (TPR): also known as Detection Rate (DR) is the percentage of the anomaly
records number correctly flagged as anomaly over the total number of anomaly records in (4):
Accuracy =
TP+TN
TP+FP+FN+TN
(1)
Precision =
TP
TP+FP
(2)
Recall =
TP
TP+Fn
(3)
DR = TPR =
TP
TP+Fn
(4)
False Positive Rate (FPR): the percentage of the normal records number wrongly flagged as
anomaly is divided by the total number of normal records in (5) [32].
FPR =
FP
FP+TN
(5)
5.3. Comparative study of three deep learning models-based intrusion detection system
The research on security issues relating to IDS exists since the birth of computer architectures.
In recent days, applying deep learning to IDS is of prime interest among security researchers and specialists.
A comparative study of three deep learning models, Inception convolutional neural network (CNN),
Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) applied to IDS.
Tables 1-3 illustrate the accuracy, precision, and recall of our three deep learning models in KDD+
and KDD-21, as we can see that the inception CNN and BLSTM exceed the DBN. Figures 4-7 provide
a comparison of the experimental results of Tables 1-3. From the results, we could find that the inception
architecture CNN got the highest overall accuracy (ACC). Besides, the BLSTM model surpassed the DBN
model on both the overall precision and overall recall rate in KDD+ and KDD-21. Although the DBN model
performed worse than the other three models on ACC, precision, recall rate in KDD+ and KDD-21,
it obviously failed on information on the attack. The proper explanation that BLSTM tried a lot on
the whole sequence comprehension and Inception-CNN could extract the key information more quickly.
Inception-CNN and BLSTM perform better than DBN. In theory, DBN should be the best model but it is
very hard to estimate joint probabilities accurately at the moment.
We found that all three models had good performance on the Normal data and DoS data and Probe.
Table 4 shows the DR of KDDTest+; Table 5 shows the FPR of KDDTest+; Table 6 shows the DR of
KDDTest-21; Table 7 shows the FPR of KDDTest-21. Results in Tables 4, 5, 6 and 7 compare DR and FPR
of KDDTest+ and KDDTest-21 we can conclude that the Inception-CNN and BLSTM provide better results
in DR and FPR compared to DBN, we can find that results of R2L and U2R are relatively quite small for all
models, which might because of the insufficiency of their records in the dataset. However, the models still
can detect some of them. Bidirectional long short-term memory (BLSTM) and Inception-CNN, are used to
detect anomalies in sequence. The results show that the Inception-CNN and BLSTM showed superiority over
the other DBN model. We tie that to the fact that Inception-CNN and BLSTM has the ability to define normal
behavior from large datasets and can be used to detect a new unseen threat. It can be concluded that if one
only wants to classify the network traffic as normal or attack, Inception CNN or BLSTM they will be
a better choice.

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Table 1. Accuracy for each model
Inception-
CNN
BLSTM DBN
KDDTest+ 88,03% 84,03% 71.91%
KDDTest–21 73.98% 75.36% 66.73%
Table 2. Precision for each model
Inception-
CNN
BLSTM DBN
KDDTest+ 85.90% 93.98% 73.86%
KDDTest–21 83.66% 77.89% 74,.65%
Table 3. Recall for each model
Inception-
CNN
BLSTM DBN
KDDTest+ 85.58% 86.01% 80.67%
KDDTest–21 72.11% 72.98% 69,23%
Table 4. DR of KDD Test+
Normal Dos Probe R2L U2R
Inception-CNN 88.468% 69.548% 61.357% 18.579% 22.348%
BLSTM 87.975% 71.576% 63.574% 29.110% 24.022%
DBN 79.697% 66.241% 57.957% 16.436% 17.043%
Table 5. FPR of KDD Test+
Inception- CNN 28.576% 28.622% 6.061% 2.178% 0.063%
BLSTM 65.448% 24.659% 10.323% 7.810% 0.082%
DBN 46.533% 25.2108% 14.073% 6.073% 1.065%
Table 6. DR of KDD Test- 21
Inception-CNN 95.870% 77.182% 67.245% 22.323% 20.819%
BLSTM 82.451% 67.584% 61.865% 20.567% 21.634%
DBN 72.211% 54.987% 60.773% 16.765% 19.984%
Table 7. FPR of KDD Test- 21
Inception-CNN 36.870% 14.53% 2.357% 0.479% 0.068%
BLSTM 51.975% 19.576% 7.574% 1.110% 0.022%
DBN 63.432% 22.653% 7.325% 2.972% 1.086%
Figure 4. Accuracy, precision and recall of
KDDTest+
Figure 5. DR of KDDTest+ using inception-CNN,
BLSTM, DBN

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Figure 6. Accuracy, precision and recall of
KDDTest-21
Figure 7. DR of KDDTest-21 using inception-CNN,
BLSTM, DBN
6. CONCLUSION
Recently, Deep learning for intrusion detection system has received much deliberation. In any IDS,
audit data samples are analyzed to set detection rules in highly mobile node network to protect against
number of novel attacks. The primary advantage of using Deep learning based intrusion detection systems is
that it is highly accurate and able to detect or categorize attacks without any environmental influence.
Different Deep learning based IDS approaches have their own benefits and disadvantages. Therefore,
considering the MANET scenarios, it is important to choose a precise method for implementing IDS.
This paper is motivated by the need to develop good training algorithms for deep architectures-based
IDS, since these can be much more representationally efficient than shallow ones such as SVMs and one-
hiddenlayer neural nets. which may be proved important for selecting the appropriate methods on bases of
the situation in MANET. In this work, the practical problems of existing IDS have been addressed and
different Deep Learning models (Inception-CNN, BLSTM and Deep Belief model) are compared to solve
these problems. The models have been implemented and tested on NSL-KDD dataset. The reason behind
the superiority of Inception-CNN in general that, it’s the ability to define normal behavior from a large
dataset and can be used to detect a new unseen threat. This work can be extended in two directions: first,
implement other deep models and create hybrid models and voting systems across different models to detect
and recognize low false alarm threats. Second, provide existing systems with real-world data for multiple
networks in such a way that the model can increase its accuracy by adapting the definition of normal
activities through different un-calibrated datasets.
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