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A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks

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M. Ezhilarasi
M. Ezhilarasi
M. Ezhilarasi
  • This person is not on ResearchGate, or hasn't claimed this research yet.
L. Gnanaprasanambikai
L. Gnanaprasanambikai
L. Gnanaprasanambikai
  • This person is not on ResearchGate, or hasn't claimed this research yet.
A. Kousalya
A. Kousalya
A. Kousalya
  • This person is not on ResearchGate, or hasn't claimed this research yet.
M. Shanmugapriya
M. Shanmugapriya
M. Shanmugapriya
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract and Figures

The application of wireless sensor networks is not limited to a particular domain. Technology advancements result in innovative solutions for simple communication to large applications via wireless sensor IoT networks. Besides the advancements, there is a serious issue in terms of threats or attacks on wireless sensor networks, which is common. Various intrusion detection methodologies have evolved so far to detect common network attacks. But it is essential to concentrate on other routing attacks like selective forwarding attack, black hole attack, Sybil attack, wormhole attack, identity replication attack, and hello flood attack. Existing research models concentrate on any one of the above-mentioned routing attacks and attain better detection performance. Detecting each attack through different detection mechanisms will increase the overall cost, and it is a tedious process. Considering this factor, in this research work, a novel intrusion detection system is introduced to detect routing attacks in wireless sensor networks using fuzzy and feed-forward neural networks. The experimental results demonstrate that the proposed model attains an average detection rate of 97.8% and a maximum detection accuracy of 98.8%, compared to existing techniques like support vector machine (SVM), decision tree (DT), and random forest (RF) models.
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FOCUS
A novel implementation of routing attack detection scheme by using
fuzzy and feed-forward neural networks
M. Ezhilarasi
1
•L. Gnanaprasanambikai
2
•A. Kousalya
3
•M. Shanmugapriya
4
Accepted: 14 February 2022 / Published online: 14 March 2022
The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
The application of wireless sensor networks is not limited to a particular domain. Technology advancements result in
innovative solutions for simple communication to large applications via wireless sensor IoT networks. Besides the
advancements, there is a serious issue in terms of threats or attacks on wireless sensor networks, which is common. Various
intrusion detection methodologies have evolved so far to detect common network attacks. But it is essential to concentrate
on other routing attacks like selective forwarding attack, black hole attack, Sybil attack, wormhole attack, identity
replication attack, and hello flood attack. Existing research models concentrate on any one of the above-mentioned routing
attacks and attain better detection performance. Detecting each attack through different detection mechanisms will increase
the overall cost, and it is a tedious process. Considering this factor, in this research work, a novel intrusion detection system
is introduced to detect routing attacks in wireless sensor networks using fuzzy and feed-forward neural networks. The
experimental results demonstrate that the proposed model attains an average detection rate of 97.8% and a maximum
detection accuracy of 98.8%, compared to existing techniques like support vector machine (SVM), decision tree (DT), and
random forest (RF) models.
Keywords Intrusion detection Routing attacks Wireless sensor networks Neural networks
1 Introduction
The application of wireless sensor IoT networks has
increased rapidly over the past decade. It is widely used in
environmental monitoring, geographical sensing, home
automation, traffic control, and other industrial applica-
tions. The sensors are deployed even in remote locations to
monitor and observe the changes. The sensors collect the
information and transmit it to the base station for further
processing. However, due to the limited energy capacity,
memory, bandwidth, and processing structure, they are
vulnerable to attacks. Depending on the attacking nature,
the attacks are mainly categorized into active and passive
attacks. In a passive attack, the intruder listens to the
communication channel as an unauthorized attacker and
monitors the data transmission. Whereas in active attacks,
the intruder monitors the channel and tries to modify the
channel data transmission. Some of the most significant
active and passive attacks are discussed here in order to
provide an ideology about the attacking nature. The major
passive attacks are monitoring and eavesdropping, homing
attacks, traffic analysis, and camouflaging adversaries.
Communicated by V Suma.
&M. Ezhilarasi
ezhilarasi.nagarajan@srec.ac.in
L. Gnanaprasanambikai
gnanaprasanambikai.lakshmanan@kahedu.edu.in
A. Kousalya
kousalyaa@skcet.ac.in
M. Shanmugapriya
shanmugapriya_sf175@anjaconline.org
1
Department of EEE, Sri Ramakrishna Engineering College,
Coimbatore, India
2
Department of Computer Science, Karpagam Academy of
Higher Education, Eachanari, India
3
Department of IT, Sri Krishna College of Engineering and
Technology, Kuniyamuthur, India
4
Department of Computer Science, Ayya Nadar Janaki
Ammal College, Sivakasi, India
123
Soft Computing (2023) 27:4157–4168
https://doi.org/10.1007/s00500-022-06915-1(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Flow Misleading: Worm-Hole Attack in Software-Defined Networking via Building In-Band Covert Channel
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Link Layer Discovery Protocol (LLDP), which is widely used by the controller in Software-Defined Networking to discover the network topology, has been demonstrated to be unable to guarantee the integrity of its messages. Attackers could exploit this vulnerability to fabricate LLDP packets to declare a false link connecting two distant switches to the controller. By doing so, the controller would be misled to route flows to the false links, which leads to further DoS, eavesdropping and even hijacking attacks. This attack seems very similar to the well-known Worm-Hole Attack in wireless sensor networking (WSN). Nevertheless, in WSN, attackers are assumed to leverage an out-of-band wired channel to achieve the true packet transmission between the two cheating sensor nodes. Unfortunately, in SDN, there usually does not exist any out-of-band channels between the distant cheating switches. Flows misguided to the fake link will cause 100% packet loss, and thus be detected soon. In this paper, we address this problem and propose the first True worm-hole attack in SDN, which could achieve packet transmission over the forged link without using any out-of-band channels. Instead, it introduces a relay host in the networks to build a completely in-band covert channel between the two cheating switches. Unlike the existing studies, a relay host is not required to be directly linked to them. Moreover, attackers are only assumed to poss the remote read and write privileges of the flow tables of the both cheating switches and do not have to alter any of their software or hardware. Our extensive experiments demonstrate the high feasibility of this attack. Both the increases of transmission delays and packet loss rates are within a reasonable range. We finally present and evaluate the countermeasures against the proposed attack.
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An Extensive Validation of a SIR Epidemic Model to Study the Propagation of Jamming Attacks against IoT Wireless Networks
Article
  • Oct 2019
  • COMPUT NETW
This paper describes the utilization of an epidemic approach to study the propagation of jamming attacks, which can affect to different communication layers of all nodes in a variety of Internet of Things (IoT) wireless networks, regardless of the complexity and computing power of the devices. The jamming term considers both the more classical approach of interfering signals focusing on the physical level of the systems, and the cybersecurity approach that includes the attacks generated in upper layers like Medium Access Control (MAC), producing the same effect on the communication channel. In order to study the accuracy of the proposed epidemic model to estimate the propagation of jamming attacks, this paper uses the results of public simulations and experiments. It is of special interest the data obtained from experiments based on protocols such as Multi-Parent Hierarchical Protocol (MPH), Ad-hoc On-demand Distance Vector (AODV), and Dynamic Source Routing (DSR), working over the IEEE 802.15.4 standard. Then, using the formulation of the deterministic epidemiological model Susceptible–Infected–Recovered (SIR), together the abovementioned simulation, it has been seen that the proposed epidemic model could be used to estimate in that kind of IoT networks, the impact of the jamming attack in terms of attack severity and attack persistence.
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