IRJET- An Efficient Model for Detecting and Identifying Cyber Attacks in Wireless Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2526
AN EFFICIENT MODEL FOR DETECTING AND IDENTIFYING CYBER
ATTACKS IN WIRELESS NETWORKS
S. Gayathri[1], P. Abirami[2], K.Bakiyalakshmi[3]
1Assistant Professor, Department of Computer Science and Engineering, Jeppiaar SRR Engineering College
Tamil Nadu, India
2,3Student, Department of Computer Science and Engineering, Jeppiaar SRR Engineering College, Tamil Nadu, India
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Abstract - All communications that occur within this
technological era use various types of networks for
transmission of messages. Numerousamountofinformation is
being passed using the networks and it is very essential to
protect these networks from cyber attacks. Nowadays many
transactions are done using the wireless medium as the use of
wired transmissions involves numerous expenditures in
installation and maintenance. Use of wireless medium has
given rise to many cyber attacks in thenetwork whichneeds to
be always monitored. Numerous researcher has beenworking
on building a Network Intrusion Detection System (NIDS) in
order to detect any cyber attacks in thenetwork. Inthispaper,
we have designed a model that is able to detect any malicious
behaviors in the wireless network using deep learning
approaches. The model is designed in such a way that it isable
to do feature selection and classification for any given
network. The dataset used for evaluating the parameters of
the proposed NIDS was NSL KDD CUP. Some oftheparameters
used for finding the efficiency of the system was the detection
rate, recall, precision.
Key Words: Network, Security, Cyber Attacks, Deep
Learning, NIDS, Feature Selection.
1. INTRODUCTION
Nodes transmitting data in the form of signals between one
another in a network without any wired connections are
popularly called as Wireless Networks. These networks are
majorly implemented in the real worldtoreducethenumber
of wires that connect the various nodes in the network. The
node could be anything, an antenna or a base station that
frequently communicates with other nodes in the network
by sending or receiving signals. Broadcasting is one of the
best characteristics of wireless networks where the data is
echoed to the entire nodes in the network unless like in the
traditional network where only the receiver will be able to
receive the data. It consists of several applications and
security is provided to all the applications that are used for
communication with one another. There are various
challenges and security attacks that encountered in a
wireless network[1,2,3,4]. To avoid these numerous
techniques and routing protocols[5,6,7] are designed for
efficiently directing the packets from one node to another
within the network. Numerous intrusion detection systems
are also designed by various researchers to detect if there
are any kind of cyber attacks or malicious activities that are
occurring within the network.
The growth of Artificial Intelligence has given birth to many
new technologies out of which the popular ones are being
Machine Learning Approaches and Deep Learning
Techniques. Use of ANNs is widely called as Deep Learning
Approaches as the neural network learns each and every
layer very deeply and uses the output of a layer as the input
of the next layer. ANNs are information processing
structures that can solve any problem through learned
examples rather than pre-specified algorithms [8]. In this
paper, we have proposed a framework for identifying and
detecting various cyber attacks in a wireless network using
machine learning techniques. The proposed system is
evaluated on various parameters andisobservedtoperform
better than the existing systems. The rest of the section is as
follows: Section II consists of Literature Survey, section III
consists of the methodology used in the paperandsection III
consists of various results obtained. The paper is concluded
in the last by mentioning the relevant future works that
could be applied or added to the proposed work.
2. RELATED WORKS
Wireless communication is one among the most vibrantly
used communication technique[9] designed in such a way
that it increases thereliabilityoftheairinterface[10].Various
researchers have developed numerous intrusion detection
systems using various technologies. There are numerous
attacks that occur in a network for which these NIDS are
proposed [11]. Security is one of the important aspects that
need to study in all the possible directions as the attack may
be from anywhere [12]. Some attacks have been studied
where the attacks try to attack the estimation and control
systems where a number of sensors and actuators are
deployed [13]. Detection of integrity attacks occurring in a
network is identified by a model developed in [14]. The
possible types of attacks in replay attacks are discussed in
[15]. A topology was deployed for identifying all the possible
attacks but it gained to providethe securitytothenetworkas
discussed in [16] and [17]. In [18], Nathone Shone has
developed an Intrusion Detection model that efficiently
identifies all the malicious behavior of the network. The
model is designed in such a way that the model makes use of
Non - Symmetric Auto encoder. The system makes use of a
Random forest in order to improve the total efficiency of the

network. The system is inefficient in reducing the
dimensionality of the data and was compared with Deep
Belief Networks which yielded a better accuracy.
3. PROPOSED APPROACH
The system proposed in the following research papermakes
use of deep learning techniques where it makes use of
Random Forest Classifier. The network consists of various
layers such as the input layer, the hidden layer, and the
output layer. These layers are responsible for feature
extraction. Features of the network are trained to the
classifier of when a network is cyber attacked and when it is
not. Based on the previous training given to the classifier, it
is able to identify when a new behavior is observed in the
network and alerts the system admin about the malicious
behavior of the network. In Fig. 1 the architecture of the
proposed methodology is given. Wecanseenumerouslayers
that are responsible for feature extractioninthenetwork. All
the layers extract the features and further give the
summation of the entire network to the classifierwhichthen
classifies the behavior ofthenetwork.TheRandomClassifier
s used widely in order to make the weak learners as strong
learners. The forest that is built consists of numerous weak
learners tree. It is mainly used to increase the levels of bias
in order to make few corrections and modifications to the
network.
Fig. 1 Block Diagram of the Proposed System
The autoencoder is used in the proposed method. It is a
neural network thatfollows unsupervisedlearninginnature.
The neural network is used to learn all the available
parameters of the network in order to build a requiredinput
of the system.
Fig. 2 Sample Auto Encoder
The entire generalization of the network is obtained using
backpropagation algorithm. The autoencoder is combined
with stack NDAE where each and every input vector is
mapped step by step with its latent representations. The
sigmoid activation function is also used for generalizing the
system.
4. EXPERIMENTAL RESULTS
The experimental results were done on various datasets.
Some of the prominently used datasets are KDD Cup '99 and
NSL-KDD dataset. These datasets were used as they were
proposed as one of the prominent datasets to be used as a
benchmark in various literature. The experiment was
performed in MAT Lab R2017b where a Random Forest
classifier was used to train the network withall thebehavior
that could happen within the network. As the model was
trained it was able to efficiently identify any malicious
activities occurring within the network. Various parameters
were used for evaluating the parameters ofthemodel.InFig.
3, the error loss of the autoencoder is depicted performed
using NSL-KDD dataset.
Fig. 3 Error loss of First Non-Symmetric Deep Auto
Encoder (NSL -KDD)
Fig. 4 Input train and Test Dataset dimension
The train and test dataset are used to train the classifier and
also to test it. Various dimensions need to be given in order

to make the classifier to automatically select the features
from the dataset.
Fig. 5 Identifying Class labels and removing Low-
Frequency attacks.
The low-frequency attacks are identified and removed from
the network as shown in Fig. 5. This is done by making the
necessary class labels within the network that could help to
identify the cyber attacks. The classification accuracy of the
KDD CUP dataset obtained by using the deep learning
technique is depicted in Fig. 4. The classification accuracy of
about 82% is obtained where the system is able to correctly
detect the malicious activities or cyber attacksinthesystem.
Fig. 4 Classification Accuracy of KDD CUP dataset
5. CONCLUSION
Communication is one of the most important aspects in this
technical era. All the means of communications occur
through some or the other networking devices that tend to
form a network. The network could be either wired or
wireless. As numerous amount of information is being
transmitted via this network it needs to be protectedagainst
any kind of cyber attacks. In this paper, wehaveproposed an
Intrusion Detection System that is able to efficiently identify
the malicious behaviors of the network is present. The
identification id sonde using deep learning techniques and
by making use of the Random forest Classifier.Theefficiency
of the system is observed by making use ofKDDCUPdataset.
The system has produced an accuracy level of about 82%
and is proved to be efficient when compared to other
traditional systems..
REFERENCES
[1] G. Sabeena Gnanaselvi, T.V.Ananthan, “An Analysis of
Applications, Challenges and SecurityAttacksinMANET”,
International Journal of Computer Sciences and
Engineering, Vol.6, Issue.5, pp.941-947, 2018.
[2] Larsen, E., 2012. TCP in MANETs–challenges and
Solutions. FFI-Rapport-2012/01514.
[3] Daly, E.M. and Haahr, M., 2010. The challenges of
disconnected delay-tolerant MANETs. Ad Hoc
Networks, 8(2), pp.241-250.
[4] Ding, S., 2008. A survey on integrating MANETs withthe
Internet: Challenges and designs. Computer
Communications, 31(14), pp.3537-3551.
[5] Abolhasan, M., Wysocki, T. and Dutkiewicz, E., 2004. A
review of routing protocols for mobile ad hoc
networks. Ad hoc networks, 2(1), pp.1-22.
[6] Hong, X., Xu, K. and Gerla, M., 2002. Scalable routing
protocols for mobile ad hoc networks. IEEE
Network, 16(4), pp.11-21.
[7] Gupta, A.K., Sadawarti, H. and Verma, A.K., 2010.
Performance analysis of AODV, DSR & TORA routing
protocols. International Journal of Engineering and
Technology, 2(2), p.226.
[8] Md. Badrul Alam Miah, Mohammad Abu Tousuf,
Detection of Lung Cancer from CT Image Using Image
Processing and Neural Network, IEEE, In Proceedings of
2nd Int’l Conference on Electrical Engineering and
Information & Communication Technology, 2015.
[9] Tse, D. and Viswanath, P., 2005. Fundamentals of
wireless communication. Cambridge university press.
[10] Akkaya, K. and Younis, M., 2005. A survey on routing
protocols for wireless sensor networks. Ad hoc
networks, 3(3), pp.325-349.
[11] A. Teixeira, I. Shames, H. Sandberg, and K. H. Johansson,
“A secure control framework for resource-limited
adversaries,” Automatica, vol. 51, pp. 135–148, Jan.
2015.
[12] H. Fawzi, P. Tabuada, and S. Diggavi, “Secure estimation
and control for cyber-physical systems under
adversarial attacks,” IEEE Trans. Autom.
Control, vol. 59, no. 6, pp. 1454–1467, Jun. 2014.
[13] Y. Mo, R. Chabukswar, and B. Sinopoli, “Detecting
integrity attacks on
SCADA systems,” IEEE Trans. Control Syst. Technol.,vol.
22, no. 4,
pp. 1396–1407, Jul. 2014.
[14] M. Zhu and S. Martínez, “On the performance analysis of
resilient
networked control systems under replay attacks,” IEEE
Trans. Autom.
Control, vol. 59, no. 3, pp. 804–808, Mar. 2014.
[15] M. Zhu and S. Martínez, “On the performance analysis of
resilient networked control systems under replay

attacks,” IEEE Trans. Autom. Control, vol. 59, no. 3, pp.
804–808, Mar. 2014.
[16] A. W. Al-Dabbagh and T. Chen, “Modelling and control of
wireless networked control systems: A fixed structure
approach,” in Proc. IEEE Conf. Control Appl., Sydney,
NSW, Australia, Sep. 2015, pp. 1051–1056.
[17] A. W. Al-Dabbagh and T. Chen, “Design considerations
for wireless networked control systems,” IEEE Trans.
Ind. Electron., vol. 63, no. 9, pp. 5547–5557, Sep. 2016.
[18] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep
learning approach to network intrusion detection. IEEE
Transactions on Emerging Topics in Computational
Intelligence, 2(1), 41-50.

IRJET- An Efficient Model for Detecting and Identifying Cyber Attacks in Wireless Networks

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