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Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

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Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher's attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2-2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.
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Wireless Personal Communications
https://doi.org/10.1007/s11277-022-09776-x
1 3
Intrusion Detection System inWireless Sensor Network Using
Conditional Generative Adversarial Network
TanyaSood1· SatyarthaPrakash2· SandeepSharma3 · AbhilashSingh4·
HemantChoubey3
Accepted: 7 May 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Abstract
Wireless communication networks have much data to sense, process, and transmit. It tends
to develop a security mechanism to care for these needs for such modern-day systems.
An intrusion detection system (IDS) is a solution that has recently gained the researcher’s
attention with the application of deep learning techniques in IDS. In this paper, we pro-
pose an IDS model that uses a deep learning algorithm, conditional generative adversarial
network (CGAN), enabling unsupervised learning in the model and adding an eXtreme
gradient boosting (XGBoost) classifier for faster comparison and visualization of results.
The proposed method can reduce the need to deploy extra sensors to generate fake data to
fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters
were selected to give optimal results to our model without significant alterations and com-
plications. The model learns from its dataset samples with the multiple-layer network for
a refined training process. We aimed that the proposed model could improve the accuracy
and thus, decrease the false detection rate and obtain good precision in the cases of both
the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber
intrusions. The false alarm rate of the proposed model decreases by about 1.827%.
Keywords Wireless sensor networks· Deep learning· GANs· XGBoost· Security· IDS·
Confusion matrix
1 Introduction
Over the years, wireless networks have been used in a wide array of applications like—out-
door and indoor monitoring applications, communication, and internet services. There is
a wide variety of data being transmitted in these deployed networks. This variety of net-
works in today’s scenario comes with a dire need for security enhancements because of
the several different types of attacks and security breaches possible in today’s world. With
the continuously increasing data transmission between various entities and networks, we
also need suitable network protection strategies. There have been various security methods
* Sandeep Sharma
sandeepsvce@gmail.com
Extended author information available on the last page of the article
T.Sood et al.
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implemented and adapted in the networks until now, like cryptographic systems [1], base
system protection [2], location verification [3], intrusion verification and detection [4],
doubly near-far problem [5], and so on. The networks these days have large amounts of
data and information to be processed; this leads to the drainage of their energy and mem-
ory capacities. Wireless information and data are simultaneously transferred in wireless
networks to decrease the battery drain problem [6]. The security mechanism chosen for
these networks may lead to more energy and memory consumption, leading to the failure
of sensors, failure of a network entity, loss of data, and thus, a network failure altogether. A
solution to these limitations may lie in Intrusion Detection Systems.
An intrusion detection system (IDS) is a security application that recognizes the secu-
rity breaches by outsiders and insiders in a system. An IDS can thus be understood as a
wholesome entity that monitors the behavior of a system and responds to the abnormalities
in its functioning. Modern-day systems deal with a vast amount of data, and thus, devel-
oping more intelligent and platform-friendly solutions is necessary. The security systems
should be flexible in their operations across various platforms and provide maximum secu-
rity. Thus, a viable solution to develop such an entity that tackles these present-day prob-
lems is the integration of machine learning to ease the process of monitoring and securing
the wireless networks and take care of its limitations.
Wireless Sensor Networks (WSNs) consist of low-power sensors capable of sensing and
transmitting information through wireless channels. These are battery constraint devices,
and one must use them efficiently; otherwise, if the battery is drained, the node is dead, and
a dark zone with no network coverage occurs. The WSNs find applications in various filed
like data aggregation, information sensing, environment monitoring, digital agriculture,
smart agriculture, remote sensing, healthcare, and military applications like border surveil-
lance [7, 8]. There is always no man’s land between the line of control (LOC) between any
two nations. There are army personnel and troops deployed to patrol their areas, but moni-
toring humans is not always feasible. Any intruder crossing these crucial areas can harm
the army deployment and arrangements, causing great harm to the nation’s security. Wire-
less sensor networks are deployed to have the best possible surveillance against intrusion to
strengthen detection in vulnerable border areas [9].
For securing such fields, a typical communication system is to be established. Such
a scenario requires adding newer entities to the networks and hence generates a massive
amount of data in those networks with the communicating entities. Monitoring in these
areas is continuously done without any interruption, and hence there exists a fast-grow-
ing data exchange scenario. The challenge here is to develop a solution to increase data
transmission speeds and modernize how the entities communicate. Security is an equally
important aspect other than the communication system and needs to be addressed. Network
security is an aspect that takes care of the network and its services altogether. It protects
againstunauthorized access to unapproved network modifications and maintains the funda-
mental integrity and confidentiality between the users to protect the data exchanged within
the network. Various security measures have been adopted to secure the wireless sensor
networks from unwanted threats likecryptography, code-testing techniques, secure loca-
tion, secure routing techniques, etc. [2]. However, measures like a key exchange, firewalls,
antivirus, network analyzers, etc. [10] have worked well in the past, but these models prove
incompetent in more extensive networks.
Moreover, the previous models, like cryptographic key exchange systems, can be cum-
bersome when applied to a more extensive sensor network. They would exploit the sensor’s
limited memory and energy capacity, and key management can be a problematic task [11].
Thus, we need to use a solution to monitor a scalable network of any size at a wholesome
Intrusion Detection System inWireless Sensor Network Using…
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level [12]. One viable solution for such problems is the Intrusion Detection System (IDS).
It is a network security measure that takes over the security of the hardware and the soft-
ware involved in a network [13]. It aids in the overall protection of the network and pro-
vides us with a much easier solution. The IDS can monitor the total traffic transmitted to
the network and keep track of the system logs. Whenever it observes an abnormality or vul-
nerability in the system, it alerts the administrator. It eases the complexity of the network
and thus is more comfortable in monitoring and maintenance. The use of AI technologies
[14]and fuzzy-based decision tree mechanism [15, 16] in building such IDSs is a growing
area of research, as it can ease human intervention in network monitoring tasks [17].
Recently, drones have been deployed to secure premises of high-security zones where
the open challenge is to predict the drone’s flight’s direction of arrival (DoA) [18]. How-
ever, when we talk about a sensor-based system, the sensors are deployed randomly in clus-
ters which are formed based on any fuzzy-based [19], nature-inspired [20] or any other
conventional algorithm [21]. The node deployment plays a critical role in the performance
of the system. Node localized at the right place and with minimum localization error [22]
substantiallyboosts the system’s performance. If a node is not appropriately placed, poten-
tial intruders can enter the system and alleviate the attacks in the sensor network deployed
system. It is countered by choosing a trust-based system [23, 24]. Any IDS detects a suspi-
cious activity with the help of any technique such as machine learning [25] and deep learn-
ing, which trains the system against various scenarios. Then with the help of the trained
scenarios, any intrusion can be detected [26]. The deployed sensor nodes require a mobility
model for random movement in the region of interest. The mobility model is selected in
such a way as to decrease energy consumption and enhance the QoS of the system [27].
Author’s Contribution: In this paper, we propose one such IDS, based on Deep Learn-
ing methods, which can train itself to distinguish between normal and attack class data.
The proposed method prevents theadministrator from supervising network traffic and save
energy and time. The system would detect the fraud data itself and alert any attacks occur-
ring in the network when combined with an alerting system. This model can reduce the
number of sensor nodes in the sensor network. Moreover, it can reduce the need to deploy
extra sensor nodes to create adversary data to confuse the attacker. Our proposed system is
also capable of generating more fake data and thus, can help reduce the size of the sensor
network.
Paper Organization Section 2 talks and sums up the referred literature regarding the
various previous developments that have taken place in this area of research. Section3
deals with the proposed system model, the IDS algorithm, and the system’s basic enti-
ties. We summarize the simulation parameters and discuss the inferences drawn from the
obtained results in Sect.4. The paper is concluded in Sect.5 with its future scope.
2 Related Works
The security of wireless networks has been an open challenge for researchers over the
years. Technological advancements have aided developers and researchers in finding and
implementing newer security solutions. However, it has also led the attackers to crack the
traditional techniques more easily. For example, ransomware attacks affect a vast number
of systems worldwide. Therefore, it created a necessity to develop newer and more techno-
logically advanced models to tackle such attacks.
T.Sood et al.
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Previously, many methods have been used to develop the IDSs. Although the various
deep learning methods introduced have an excellent accuracy measure, the methods used
have their drawbacks, which may prove to be constraints in the application phase. The
authors [28] introduced an IDS using the Support Vector Machine (SVM) algorithm and
Deep Learning on the Port Scan data, similar to one part of our experiment. However,
the deep learning model depicts good accuracy over the SVM model. The SVM method
generally faces disadvantages in dealing with more massive datasets, which might pose an
issue in applying more extensive networks and more enormous datasets. In [29], the author
talks about how the deep learning techniques have established their efficacy in ML and
intrusion detection. In the image classification tasks, the systems might have discrepancies
that benefit the attackers. It might lead to having doubts about deploying the deep learning
networks in the intrusion detection field. The author has researched these deep learning
algorithms against excellent attack algorithms like JSMA, DeepFool, FGSM, etc., on the
NSL-KDD dataset. The author did not work upon transferability with different inputs in
the same neural network, between the different NNs, or with well-known ML techniques
like SVM. In [30], the authors propose SVM and Autoencoders to introduce a Network
Intrusion Detection System (NIDS) based on Self Taught Learning (STL). They manage
to obtain good classification accuracy and compare it to various other methods. However,
the autoencoders are not very competent in data regeneration; their performance is not very
satisfactory in this respect. The GANs can perform better when compared to autoencod-
ers. Deep neural networks (DNN) study various datasets [31]. The framework is a multi-
layered hybrid DNN that is to be used as an IDS. The results are reasonably accurate in all
the datasets. However, the stability of the results is not constant in the datasets, which can
be used to further improve the results by using other deep learning methodologies.
This paper is similar to [32] in using a GAN model for developing a security system.
The authors presented a sensor network security technique, which was accurate. However,
our work is different as we have developed a detection system based on the CGAN model,
which is a better way to learn from classed data. Also, our model uses XGBoost for visuali-
zation of results, unlike [32], which required a cross-platform working effort.
Further, our work shows an increase in the accuracy and precision of the output and
works on a faster and lesser complex model. It can work on a single platform for all the
tasks, from preprocessing the data to visualization results. The results thus obtained are
much faster and decrease the computational time. The proposed IDS has shown a signifi-
cantly lower false detection rate, which has been one of the constraints of the Intrusion
Detection Systems in general. The following section discusses the algorithm involved in
the proposed work and the concepts.
3 System Model
Before we discuss the system model, we need to know about the basics of our model—
GAN, CGAN, and XGBoost. The data is first taken in and trained by the CGAN model
and then passed on to the XGBoost classifier to validate the obtained results. Therefore, we
first shed light on the problem we aim to fix, explain these basic concepts, and discuss our
proposed model.
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3.1 Problem Statement
This work focuses on a new unsupervised learning algorithm and its application to secure
IDS. This framework will produce fake data to confuse the attacker. It can secure the
network and transmit data between the sender and the receiver, compared to the other
approaches of IDS developed using Deep Learning. This technique eliminates the need
for fake sensor nodes or fake data-producing entities, increasing the network’s energy and
memory consumption. The two frameworks discussed in the following subsections and
considered after the literature survey have been found to have limited research on the pos-
sible applications of the CGAN and XGBoost. Thus, we take on the task of presenting an
amalgamated framework of the two algorithms and studying their results.
3.2 Generative Adversarial Networks (GAN)
A ‘GAN’ is a neural network that can create new data without any prerequisites or from
scratch. In the original formulation of GANs, proposed by Ian Goodfellow etal. in [33], the
discriminator network model generates an estimated probability that if a given image/data
was real or generated. The discriminator would then have access to both the generated and
actual data, which would lead it to generate the estimate about both types of inputs. The
discrepancy between the discriminator’s results and the actual values is calculated as the
loss. The G network is intended to get familiar with the organization of the training data. In
contrast, the D network is intended to find the similarity of the data coming from the train-
ing (real) data instead of the generator data (attack/fake).
Figure1 is a representation of the GAN’s working. As we can see from the figure,
the noise input (z) is taken and mapped into a latent space, usually real data size. Then,
this noise is input into the Generator (G) and processed according to the network condi-
tions defined. This output ‘test’data becomes the input to the discriminator (D), which
compares the real data (x) and the Generator’s test data,and then gives the output. The
output of D is in the form of weights assigned to the results, like 0 for ‘fake’data and 1
for ‘real‘data. This output is then fed back to the G network and D network. G adheres
to
Ds
feedback to improve the generated data, and this process goes on for the defined
Fig. 1 Two model training in GAN
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number of steps. These G and D networks work in union to learn from the feedback and
thus, be able to generate new data [34].
The generator’s capacity to create new data that resembles genuine examples is
improved. The perception is to bewilder the intruder/ attacker and keep them from dif-
ferentiating between the data from the generator and the valid data from the datasets.
The Discriminator differentiates between real and fake data [32] by enhancing the prob-
ability of the valid data to ‘1’and limiting the probability of fake samples being ‘0’.
Conditional Generative Adversarial Network (CGAN)
Conditional GAN is a variant of GAN, a Machine Learning structure for the train-
ing of the generative models. The authors introduced this concept in [35]. The CGAN
architecture allows the generator and discriminator to train themselves with supplemen-
tary information, like class labels or other data. There should be some extra data or
information over which the generator and discriminator should be fed to train the model
better. The conditional training can be done in a CGAN if we provide this extra data to
the generator and the discriminator. These work well for the data where we have condi-
tional requirements, and it works well with data like text and images [36]. This model
thus helped us in developing our IDS. The functioning of the CGAN is not different
from the underlying GAN and differs only in the selection of data to be input. The
basic structure of the CGAN, as proposed in [35], is shown with some modifications in
Fig.2.
Fig. 2 CGAN model
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3.3 eXtreme Gradient Bossting Algorithm (XGBoost)
XGBoost is a decision-tree-based Machine Learning (ML) algorithm that uses a gradient-
boosting algorithm structure. In prediction problems or challenges, including unstructured
information, Artificial Neural Systems will, in general, beat every other algorithm or struc-
ture [37]. Nonetheless, about little to medium organized/tabular data, decision-tree-based
calculations are viewed as top-tier. XGBoost is a sparsity-aware algorithm for irregular/
sparse data for an estimated tree-based learning algorithm [38]. It is a scalable framework
that supports the majority of scenarios.
It is also a preferred classification model due to the following advantages that it offers
[39].
It is about ten times faster than the previous classification methods.
It enables parallel processing by using various cores for the processing.
Cross-validation is a parameter that already exists in the framework, and there is no
need to install any external package for it.
It can deal with missing values, unlike Logical Regression.
XGBoost can deal with over-fitting problems and also flooding of data as happens in
case of DDoS attacks.
Tree pruning, i.e., reducing the size of a tree by removing the parts that offer none or
significantly fewer instances for classification, is done till the maximum depth of the
tree and does not only terminate after achieving a negative gradient.
The user can define various evaluation metrics and an objective function of choice.
3.4 Datasets
The two datasets studied, chosen, and considered for experimenting with to study the func-
tioning of our IDS are mentioned below:
NSL-KDD dataset: The NSL-KDD dataset [40] is one of the many datasets availa-
ble for cybersecurity study and experimentation and has a variable ratio of attack and
standard class data. This dataset is a successor of the KDD99 dataset, which had prob-
lems like redundant and duplicate data. The NSL-KDD dataset has 42 attributes, which
contain labels such as protocol type, service, and label (attacks/normal). We have
selected this dataset to be one of the two datasets for obtaining our IDS results, as this
dataset is often used as a benchmark in various studies of IDSs. The training set con-
sists of 67,343 standard class data and 58,630 attack class data.
CICIDS2017 dataset: The CICIDS2017 dataset [41] is another such dataset present
for the studying of cybersecurity and developing newer network protection systems.
This dataset is up-to-date with the latest attack information. The information labels
include source and destination IP addresses and attack labels like DDoS, PortScan,
Infiltration, etc. A dataset is a benchmark dataset if it covers 11 criteria, for example,
complete traffic, labelled dataset, attack diversity, etc. This dataset has many different
datasets labelled with different attacks that have been monitored over various systems.
It consists of 78 attribute labels. We have only used one of these datasets to study in our
model. We have considered the ‘Friday-WorkingHours-Afternoon-PortScan’ dataset to
study our second batch of data to obtain the system’s results. The PortScan attack sends
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the client request to several server port addresses that might be present on a host [42] to
find a vulnerable active port and then exploit the services it provides. This dataset has a
total of 1,27,537 ‘BENIGN’ class data and about 1,58,930 ‘PortScan’ attack class data.
3.5 Work Flow
The system workflow (Fig.3) depicts the constituent blocks that form the main founda-
tion of our work. First, the dataset is input into the network; we take the NSL-KDD
dataset and the CICIDS2017 dataset to our framework. The data is then passed onto pre-
processing. At this stage, data is usually unclean i.e. data might have missing values; the
data may have classes that the other program may not support. We pre-process the data
and convert it into a binary form using data science operations. We check for missing
values and even them out using suitable methods. Binarization is the labeling of string
data about our data.
After the pre-processing, data is sent to the CGAN model, which uses a Generator
and a Discriminator. The Generator is used to generate data samples, while the Dis-
criminator network can be understood as a critic network used to correct the generator
outputs.
We then obtain the output of the CGANs’ training, compare the generated fraud data
with the actual fraud data, and obtain the accuracy measure of this generated data; we
move on to the next step in our framework. It is the step where we utilize the XGB
Classifier for the data classification and results from visualization. This classifier recog-
nizes the different classes of data present in the dataset, defines them, and provides the
visualization results of the same-this classification and visualization of the data aid in
understanding the patterns in the data under consideration. Here, the XGBoost Classi-
fier helps us rapidly while not requiring many program run times. This data classifica-
tion also helps to recognize how efficiently the system generated thefraud data to detect
the actual fraud data.
We use some samples of the original fraud data and some of the generated fraud data
and use them as inputs to the classifier to obtain accuracy. The data accuracy measured
by the classifier is the accuracy of our proposed IDS. We also check the classifier’s
accuracy, which tells us if the classifier could recognize the data correctly and to what
measure.
Fig. 3 System workflow
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3.6 Proposed IDS Algorithm
The proposed algorithm aims to create an IDS that works faster and better than the pre-
viously developed or introduced models.
The algorithm begins by introducing the inputs and outputs for the system’s working
under consideration. These are the fundamental entities that will help build the system as
it is supposed to be to achieve our research objectives. The datasets taken in as the inputs
to the system are NSL-KDD and CICIDS2017 datasets. The specifications of both data-
sets, their inclusive entities, etc., have been discussed later. Now, as mentioned in the
algorithm, the inputs are taken according to the specifications of the system we want to
administrate. The working of the two models, as in the algorithm, is explained below. In
the algorithm, input samples from dataset X are fed to the CGAN network. These samples
are taken according to a mini-batch size, i.e., some samples would be randomly selected
according to the number defined for the mini-batch. Then, we define how many steps we
need to complete the training for the functioning to start, and this can be tailored according
to one’s need for refining the results. The number of iterations mentioned is the number of
steps the modelrequires to complete training. The generator network then starts to take
noise samples, n, in the defined number, and tries to mould them into the form of the real
data. After this first step, it passes the data to the discriminator to compare and check with
the real data. The discriminator checks this data by the generator and compares it with the
real data samples x, which are equal to the noise samples. The discriminator gives feedback
to the generator about how real or fake this data was, by assigning weights to the results
as 0 for fake and 1 for real. The generator takes this input from the discriminator and then
repeats the process until the number of steps is completed. The next step is the classifier.
The classifier has a certain number of samples (m) from the actual data in the dataset and
similar data from the generated output by the CGAN. Then, the classifier compares the two
types of data given to it and then provides us with the accuracy of the data generated by the
CGAN.
The next step is the classifier. The classifier has a certain number of samples (m) from
the actual data in the dataset and similar data from the generated output by the CGAN.
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Then, the classifier compares the two types of data given to it and then provides us with the
accuracy of the data generated by the CGAN.
The classifier can produce a confusion matrix to tell us about the CGAN’s performance
and how accurately and precisely it has generated the data. It tells us about the class-wise
distribution of data in the created set, i.e., it may reveal whether the samples created that
are said to be ‘Normal’data belong to the ‘Normal’data class, and so on. We will explain
the confusion matrix by referring to a dummy matrix in the next section.
4 Simulation Results andDiscussions
The proposed model has been implemented using Python, and the experimentation has
been carried out using Keras 2.3.1 and Tensorflow 2.1.0. We have supported the scikit-
learn, Seaborn, and XGBoost features for evaluation and result extraction. The work
has been carried out on a system equipped with Intel(R) Core(TM) i5 CPU@2.5 GHz
processor. We used a Python 3 Jupyter Notebook 6.0.3 Platform for the coding and
implementation.
We discuss the simulation settings, evaluation parameters, and the results obtained in
the upcoming sections.
4.1 Generator Network Parameters
The Generator’s function generates fake samples from the dataset samples fed to it. G will
improve its results based on the feedback received from D. We created the Generator with
four fully connected layers. Reshaping of the data was done according to the binary class
data. We took a mini-batch size of 128 and employed an Adam Optimizer on the model,
with a 0.0001 learning rate momentum of 0.9 for 5000 steps. Refer toTable1 for further
details regarding the simulation parameters.
4.2 Discriminator Network Parameters
Discriminator receives inputs from both G and real samples of the dataset and aims to dif-
ferentiate between them. G and D are trained while contesting with each other. The learn-
ing rate was specified as 0.0001, using the Adam Optimizer, the activation function as
tanh, mini-batch size was specified as 128, and the values of
𝛽1=0.5
and
𝛽2=0.9
. Here,
Table 1 Simulation parameters of generator network
S. no. Parameter Description Value
1. Mini-batch size Creates small batches of the entire data of the size
defined here
128
2. Optimizer Attribute to reduce losses Adam
3. Learning rate Enables to traverse the data slope while being able to
cover the data points
0.0002
4. Activation function Adds non-linearity to function tanh
5. Momentum Helps in faster convergence of vectors 0.9
6. Layers in the network Receiving and processing inputs happens here 4
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𝛽1
and
𝛽2
are decay rates of the moving averages of the gradients.
𝛽1
controls the exponen-
tial decay of the moving average for the first moment, whereas
𝛽2
does the same for the
second moment.
4.3 XGBoost Parameters
The XGBoost Classifier is ten times faster than the other decision-tree-based algorithms.
It is also accurate and provides excellent results on sparse or tabular data such as ours. In
the XGBoost Classifier, various parameters can be used and tuned according to one’s own
needs and the tasks desired to be performed using the classifier. The parameters we used
for our system are listed below, with a short description of their purpose and the value we
set for the same.
objective: It is used in defining the loss function that needs to be minimized.
max_depth: It defines the tree-depth. The model gets more complicated with the
increase in the tree depth. Bigger models require more considerable tree depth.
eval_metric: It is used to calculate the model’s accuracy on the testing data.
The values of these parameters are tabulated in Table2 for ready reference.
4.4 Confusion Matrix
The confusion matrix is a vital source of information about network performance in any
Artificial Intelligence application/algorithm. The basic structure of a binary-class confu-
sion matrix is shown in Fig.4. There are values distributed over two main classes of data
that are actual and predicted values. These values then get divided up to form the four lead-
ing constituent labels of the confusion matrix, namely—True Positive (TP), False Positive
(FP), True Negative (TN),and False Negative (FN). The ‘positive’ value in the case of
our data would be the ‘Normal’ class data, and the ‘negative’ value would be that of the
Attack’ class data. The significance of the four labels is, as explained: True Positive value
is assigned to a sample if the sample classified by the model as a positive sample is positive
in the dataset, i.e., its actual value is also positive. True Negative is assigned to a sample
if the model has classified the sample as a negative sample. It does so due to the negative
class in the dataset. A False Positive value occurs when the predicted results differ from the
actual results. When the expected value infers a positive value, the real value is a negative
class value. TheFalse Negative result is the opposite of the False Positive label. The out-
come predicted as unfavourable by the model is the actual class data in the original dataset.
The confusion matrix is responsible for visualizing the output quality of the model. The
classification outputs of a dataset shown in a confusion matrix are the vital components of
Table 2 XGBoost Parameters Parameter Value
Objective Binary:logistic
Max_depth 4
Eval_metric Auc
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measuring the correctness of the model’s predictions. Refer toFig.4, which represents the
confusion matrix, when studied, shows that the elements in the blue diagonal boxes repre-
sent the correct/truly classified labels. The items in the pink boxes represent the incorrectly
classified labels by the model. A higher count of values in the diagonal (blue) boxes results
in a better confusion matrix of the generation/classification/prediction model, resulting in
better model accuracy.
Using the different parameters—TP, TN, FP, and FN, of the confusion matrix, one can
calculate various measures related to finding how good the model is a prediction/classifica-
tion performance was. Several steps help to do so, for instance, Recall, Sensitivity, etc. We
are only concerned with calculating the Accuracy and Precision of the model, which are
formulated asfollows:
It measures the accuracy and precision of the model, which we intend to find out to check
the correctness of our proposed IDS.
4.5 Results
The model proposes to build an intrusion detection system. The IDS we propose is
based on the generative adversarial network algorithm and uses the XGBoost algorithm.
We have carried out the experiments for 5000 samples of data from both datasets. We
varied the dataset feature sizes to obtain results on the different values of the dataset.
The variations in the dataset result in limiting the amount of available data for training
and testing, which further affects the training and testing of the model, as it has lesser
(1)
Accuracy
=
(TP +TN)
(TP +FP +TN +FN)
(2)
Precision
=
TP
(TP +FP)
Fig. 4 Confusion matrix
Intrusion Detection System inWireless Sensor Network Using…
1 3
instances to learn from and lesser instances to compare. Therefore, we change the fea-
ture size of the datasets to see the effect of this change and check our system’s response
to these conditions. We establish whether or not the accuracy and precision of our sys-
tem are affected by this change and whetherthe system is stable for these changes. The
accuracy is calculated according to Eq.1 and the precision according to Eq.2. These
results are depicted and discussed below.
For NSL-KDD dataset
These results are obtained while experimenting with the feature size of the NSL-
KDD dataset. We experimented with the feature sizes at the original 40 feature size,
30 feature size, and 20 feature size. The plots for the losses are shown in Fig.5.
These losses depict that our model has converged the losses during the learning
process and has reached an optimum value where learning is completed. Therefore,
it depicts the stability of the training of our model. The results for the confusion
matrix obtained for the original feature size, i.e., 40, can be seen in Table3. The
confusion matrices obtained for the varied feature sizes 30 and 20 can be seen in the
Tables4 and 5, respectively. We have summed up their accuracies and the precision
measures in Table6. We observed minute changes in the accuracies across all the
feature sizes and obtained almost accurate predictions in the case of the varied fea-
ture sizes. The precision measure, which tells the correctness of the obtained posi-
tive classes, is constant. Thus, we could reduce the false detection and have obtained
Table 3 Confusion matrix for 40
features Pred 0 Pred 1
True 0 1176 0
True 1 1 1175
Table 4 Confusion matrix for 30
features Pred 0 Pred 1
True 0 353 0
True 1 2 351
Table 5 Confusion matrix for 20
features Pred 0 Pred 1
True 0 242 0
True 1 2 240
Table 6 Accuracy and precisions
for NSL-KDD data set Table number Title Accuracy (%) Precision (%)
Table3For 40 features 99.95 100
Table4For 30 features 99.71 100
Table5For 20 features 99.58 100
T.Sood et al.
1 3
good accuracy and precision measures for the same. We might, therefore, say that
the system performs well and is stable across varied conditions.
For CICIDS2017 dataset
We particularly considered the ‘Friday-Working Hours-Afternoon-PortScan’ data-
set from the many datasets available in CICIDS2017. We considered the different fea-
ture sizes, and as this is a wider and much bigger dataset than NSL-KDD, we took
the liberty to experiment with more changes in the feature sizes. Hence, we found the
results for feature size 78, feature size 68, feature size 58, and feature size 48. We also
depict the losses, and we may observe that the losses have converged to similar values
throughout the 5000 step training, and the system has reached an optimum stage. The
lossesare shown in Fig.6. The results obtained for the original feature size, i.e.,78, can
be seen in Table7. The confusion matrices obtained from varying the feature sizes to
68, 58 and 48 can be seen in the Tables8, 9 and 10, respectively. We have mentioned
all their accuracies and precision in Table11. We could observe the changes in the
accuracies for this dataset as well. Across all the feature sizes, the accuracy measure
was almost accurate, and the false detection was minimal. We could thus, reduce false
detection by 1.827% and have obtained good accuracies for the same. We might, there-
fore, say that the system performs well and is stable across varied conditions.
We can draw upon the results obtained through both the datasets and infer that the
decrease in dataset features is a parameter that can create a difference in the model’s over-
all accuracy. Thus, we can observe changes in our accuracy and precision measures and
all the used values. The losses converged in both cases, thus, implying that the system has
been optimally trained. We might also observe that the data sets have played a role in the
Fig. 5 Losses for NSL-KDD dataset
Intrusion Detection System inWireless Sensor Network Using…
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Fig. 6 Losses for CICIDS2017 dataset
Table 7 Confusion matrix for 78
features Pred 0 Pred 1
True 0 1234 1
True 1 0 1235
Table 8 Confusion matrix for 68
features Pred 0 Pred 1
True 0 740 1
True 1 0 741
Table 9 Confusion matrix for 58
features Pred 0 Pred 1
True 0 600 0
True 1 1 599
Table 10 Confusion matrix for
48 features Pred 0 Pred 1
True 0 493 1
True 1 0 494
T.Sood et al.
1 3
model’s changing accuracy and precision. The precision measures are better in the case of
NSL-KDD. However, we get better accuracy using the CICIDS2017 dataset than the NSL-
KDD dataset. It may be the case because the NSL-KDD has much lesser attribute classes
of data than the CICIDS2017 dataset, which leads to better data availability for the training
and, thus, provides better accuracy. We compiled all the accuracy and precision measures
that we obtained from the experiments over different values and have presented them in an
orderly fashion in Tables6 and 11.
5 Conclusion
This work aims to propose an Intrusion Detection System based on CGAN and the eXtreme
Gradient Boosting Algorithm. Our proposed framework is a robust IDS based on Deep
Learning to give it an advantage over other previously introduced IDSs (as referred toin
Sect.2), which may not be competent in today’s rapidly changing and growing environ-
ment. A self-learning model proves to be more accurate when provided with more samples.
The quality of samples in the running batch may also affect the accuracy and precision of
the system. The proposed IDS provides excellent accuracy and precision under given con-
straints; besides, the model was applied with different datasets and dimensions. We show
the model’s efficacy in the ‘Accuracy’ and ‘Precision’ measure of the system. We get a
decent value for both these parameters with the model that we have developed and altered
for the said conditions. The proposed model can reduce the average number of sensors
deployed by about 1.2–2.6% for our selected features, deployment strategy, and distribu-
tion, along with the false alarm rate that shows a reduction of 1.827%. The limitation of
the proposed work is that due to the non-availability of a higher-end computing system,
we could not study the model for a more extensive dataset with more samples. However,
the samples chosen offer a considerable range of fraud and standard data to the training
model. The future scope of the research may involve considering the usage of the complete
datasets and keeping the features constant. One might also check the model’s work by sub-
jecting it to changes in the CGAN model. Also, changing the layer density of the neural
networks can be studied for further work and betterment of the IDS.
Acknowledgements We want to acknowledge Madhav Institute of Technology & Science Gwalior, Gautam
Buddha University Greater Noida, and IISER Bhopal for providing institutional support. We thank the edi-
tor and the anonymous reviewers for providing us with their helpful comments and suggestions to finally
improve the manuscript in the current form.
Funding This research received no external funding.
Data Availability Data will be made available on reasonable request to the corresponding author.
Table 11 Accuracy and
precisions for CICIDS dataset Table number Title Accuracy (%) Precision (%)
Table7For 78 features 99.95 99.91
Table8For 68 features 99.93 99.93
Table9For 58 features 99.91 100
Table10 For 48 features 99.89 99.92
Intrusion Detection System inWireless Sensor Network Using…
1 3
Declarations
Conflict of interest We also declare that we do not have conflicts of interest with any person or agency.
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institutional affiliations.
Intrusion Detection System inWireless Sensor Network Using…
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Tanya Sood received her Integrated Dual Degree (B.Tech. and
M.Tech.) in Electronics and Communication Engineering with special-
ization in Wireless Communication and Networks in 2020 from Gau-
tam Buddha University, Greater Noida, India. She is currently working
as an analyst at IQVIA. Her research interest includes WSNs and their
applications, data analysis, deep learning, and machine learning. She is
currently working with IoT applications in healthcare.
Satryartha Prakash received his Integrated Dual Degree (B.Tech. and
M.Tech.) in Electronics and Communication Engineering with special-
ization in Wireless Communication and Networks from Gautam Bud-
dha University, Greater Noida, India. He is currently working as a Sen-
ior Project Fellow at CSIR-Institute of Genomics and Integrative
Biology, New Delhi, India.
Sandeep Sharma received a B.Tech. Degree in Electronics Engineer-
ing from RGPV, Bhopal, India, in 2001 and an M.Tech. Degree in
Digital Communication from Devi Ahilya University, Indore, India, in
2005. He is a Ph.D. in Electronics and Communication Engineering
from Gautam Buddha University, Greater Noida, India, in 2016. Cur-
rently, he is an Assistant Professor in the Department of Electronics
Engineering at Madhav Institute of Technology and Science, Gwalior
(M.P.), India. Before joining MITS Gwalior, he was with Gautam Bud-
dha University, Greater Noida. His research interest includes wireless
sensor networks, wireless network security, physical layer authentica-
tion, intrusion detection in wireless networks, cross-layer design, and
machine learning applications in WSNs.He has published 40 research
papers in reputed international journals and more than 43 papers pub-
lished in international conferences.He also authored 6 book chapters
which were published with reputed publishers like Springer, Taylor &
Francis CRC Press. Dr. Sharma is a recipient of the Best Conference
Paper in the international conference ICCCS, 2016, and the Young
Scientist Award in 2019 for his research work. He is an active reviewer of IET Communications, IEEE
Wireless Comm. Letters, Journal of Information Technology (Springer), Personal and Ubiquitous Comput-
ing (Springer), Multimedia Tools and Applications (Springer), International Journal of Computer Applica-
tions in Technology (Inderscience), International Journal of Communication Systems (Wiley), Journal of
The Institution of Engineers (India): Series B, Journal of Intelligent and Fuzzy Systems, Neural Networks
(Elsevier),Expert System with Applications (Elsevier), Soft Computing (Elsevier),Artificial Intelligence
Review(Springer), Sensor (MDPI),Electronics (MDPI), Designs (MDPI).
T.Sood et al.
1 3
Abhilash Singh received his Integrated Dual Degree (B.Tech. and
M.Tech.) in Electronics and Communication Engineering with special-
ization in Wireless Communication and Networks in 2017 from Gau-
tam Buddha University, Greater Noida, India. He is currently working
on his Ph.D. Degree in Remote Sensing from the Indian Institute of
Science Education and Research Bhopal, Bhopal, India. Since 2018,
he has been working on a NASA-ISRO Synthetic Aperture Radar
(NISAR) project at IISER Bhopal, Bhopal, India. He has been publish-
ing research articles in peer-reviewed conferences and internationally
reputed journals. His current research interest includes microwave
remote sensing, machine learning, bio-inspired algorithms, wireless
sensor network, and wireless communication. Mr. Abhilash was a
recipient of gold medal awards from the University for being a first
rank holder in his UG and PG. He received the prestigious "DST-
INSPIRE" Fellowship to carry out his doctoral degree from the
Department of Science and Technology (DST), India’s Ministry of
Science and Technology. He also received the DAAD fellow-
ship,travel grant from American Geophysical Union (AGU), and AGUEcohydrology Early Career Tiny
Grant. Recently, he gotfeatured as a ’leaf’in themeet a leaf series of AGU ecohydrology section.He is an
active reviewer of Remote Sensing of Environment,Artificial Intelligence Review,Complex & Intelligent
Systems,Journal of Intelligent and Fuzzy Systems,Advances in Space Research,IEEE Access,The Journal
of Open Source Software, Wireless Personal Communications, Journal of The Institution of Engineers
(India) Series B, andJournal of Ambient Intelligence and Humanized Computing.He is a member of IEEE
(student), European Geophysical Union (EGU), American Geophysical Union (AGU), ISPRS, and the
Indian Radio Science Society (InRaSS).
Hemant Choubey received a B.Tech. Degree in Electronics and Com-
munication Engineering from RGPV, Bhopal, India, in 2009 and an
M.Tech. Degree in Digital Communication from University Institute
of Technology, RGPV, Bhopal,India, in 2013. He is a Ph.D. in Elec-
tronics and Communication Engineering from MANIT, Bho-
pal,India, in 2020. Currently he is working as an Assistant Professor
in the Department of Electronics Engineering, MITS, Gwalior, India.
Before MITS, he was with TIT&S Bhopal, India as a Head of Elec-
tronics and Communication Engineering Department during 2016–
2021.His research interests include Digital Communication, Biomedi-
cal Signal Processing, and Chaotic Communication. Dr. Choubey has
published many papers in reputed international journals and confer-
ences.He also on the board of Editor and reviewer of several reputed
international journals. He is also a member of many international
bodies.
Authors and Aliations
TanyaSood1· SatyarthaPrakash2· SandeepSharma3 · AbhilashSingh4·
HemantChoubey3
Tanya Sood
tanya27alt@gmail.com
Satyartha Prakash
satyartha.p@igib.in
Abhilash Singh
sabhilash@iiserb.ac.in
Hemant Choubey
hemantchoubey271986@gmail.com
Intrusion Detection System inWireless Sensor Network Using…
1 3
1 School ofInformation andCommunication Technology, Gautam Buddha University (GBU),
GreaterNoida, UttarPradesh201312, India
2 CSIR- Institute forGenomics andIntegrative Biology (IGIB), NewDelhi110025, India
3 Department ofElectronics Engineering, Madhav Institute ofTechnology andScience,
Gwalior474005, India
4 Fluvial Geomorphology andRemote Sensing Laboratory, Indian Institute ofScience Education
andResearch Bhopal, Bhopal462066, India
... Deep learning is a subset of machine learning algorithms which has been applied for intrusion detection using WSNs Amutha et al., 2021b;Singh et al., 2022a;Sood et al., 2022;Singh et al., 2022b). It is also employed for pattern matching and network security where it identifies the malicious activities occurring in the network and is termed as Network Intrusion Detection System (NIDS). ...
Preprint
Full-text available
Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.
... Deep learning is a subset of machine learning algorithms which has been applied for intrusion detection using WSNs Lee et al., 2021;Singh, Amutha, Nagar, Sharma, & Lee, 2022a, 2022bSood, Prakash, Sharma, Singh, & Choubey, 2022). It is also employed for pattern matching and network security where it identifies the malicious activities occurring in the network and is termed as Network Intrusion Detection System (NIDS). ...
Article
Full-text available
Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.
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