An Efficient Secured AODV Routing Protocol to Mitigate Flooding

and Black Hole Attack in VANETs for Improved Infotainment

Services

Shaik Shafi, M. Anusha and Chandan

Electronics and Communication Engineering, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India

Keywords: AODV, Black Hole, VANET.

Abstract: The latest developments in wireless communication encourages the researchers to focus more in the expansion

of Vehicular Ad hoc Networks (VANETs), offers the required infotainment facilities. Nevertheless, because

of decentralized architecture, design of secure transmission is still a challenging problem in VANETs. Due

to security issues, there may be loss of communication between high speed vehicles. Thus, it is obligatory to

find and prevent such security issues. Thus, in this paper, Secure AODV Routing Protocol (S-AODV) to

mitigate Flooding and Black Hole Attacks in VANET is proposed. In this, primarily, the cooperative

intermediate vehicles are chosen based on Congestion and Residual Energy values at each vehicle to avoid

unnecessary transmission (Flooding) of huge number of routing packets to non-existent destinations. Thus

reduces routing overhead and routing cost. Secondly, the optimal secure path to the destination is identified

through trust estimation among the selected relay vehicles in the network. Here, the trust value is estimated

using distinct metrics like Hop Count and Network Lifetime. Then, the vehicles with utmost trust value are

preferred to reduce packet drop rate (Black Hole attack). Thus improves packet delivery ration and throughput

of the network. The performance of the proposed S-AODV is carried out using NS (Network Simulator)-2

and compared over existing routing schemes under attack. Simulation results showed that the proposed S-

AODV outperforms over existing AODV based routing schemes for different network parameters like, delay,

energy factor, reliability and packet delivery ratio. Therefore, the infotainment services like emergency and

multimedia message transmission is improved in VANE.

1 INTRODUCTION

VANET is an infrastructure less self-configuring ad-

hoc network, consists of mobile nodes and connects

wirelessly (Yo and Kim, 2011). Thus, maintaining the

reliability in VANETs is a challenging task as the

vehicles would reduce the network lifetime and leads

to link failures due frequent topology changes. In

addition to frequent link failures, security in VANETs

is another major issue to be addressed due to its

wireless channel vulnerability to many security

threats (Singh and Nand, 2016). Towards this, there

exists attacks in the network layer for two main

reasons: One is not transmitting the data packets and

the second is to change parameters of the control

messages. A simple attack that blocks sending the

data packets. Due to this, any malicious intruder may

act as routing agent and interrupt the entire network

operation. Thus, identifying this kind of malicious

nodes is very challenging within the network over any

other attacker from outside. Towards this researchers

have proposed several secure routing approaches

along with futuristic research challenges in high

speed mobile networks.

VANET routing schemes are categorized into

three types, namely reactive routing, proactive

routing and hybrid routing (Murthy and Manoj,

2004). Among these, authors have proposed that the

reactive routing scheme known as Ad-hoc On-

demand Distance Vector (AODV), an efficient

protocol for high density VANET (Shafi and D V

Ratnam, 2018).

However, in the process of selecting relay

vehicles, the AODV protocol may be susceptible to

some routing attacks, this is due to the presence of

single or multiple untrusted nodes in the network.

Thus, Black Hole attack, a kind of Denial of Service

(DoS) attack occurs frequently when attacker acquire

and re-program the mobile vehicles to block packets

Shaﬁ, S., Anusha, M. and Chandan, .

An Efﬁcient Secured AODV Routing Protocol to Mitigate Flooding and Black Hole Attack in VANETs for Improved Infotainment Services.

DOI: 10.5220/0012517000003739

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Artiﬁcial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics (AI4IoT 2023), pages 447-453

ISBN: 978-989-758-661-3

447

instead of sending to destination. Therefore, the

information does not reach the destination which

generates longer delay in delivering packets and

completely reduces the network throughput. In

addition, unnecessary transmission of routing packets

takes place during selection of relay vehicles towards

destination. The unnecessary transmission leads to

flooding attacks in the network (Shafi et.al, 2023).

Thus, in this paper, Secure AODV Routing

Protocol (S-AODV) is presented. Here, the concept

of trust estimation is added to the existing AODV

protocol to detect attacks in Vehicular ad-hoc

networks. In comparison with traditional AODV, the

proposed S-AODV includes multi-layer structure to

extract the various routing parameters from different

layers. Therefore, proposed S-AODV scheme

exhibits more effective in addressing the intrusion

detection issue.

The remaining portion of the paper comprises of

several sections. Section 2 illustrates the existing

works. Section 3 demonstrates the proposed S-

AODV protocol. Then, the performance study of the

S-AODV is examined in section 4. Section 5

concludes the paper.

2 LITERATURE REVIEW

In this section, the existing routing protocols to

defend flooding and black hole attacks in ad-hoc

network are explained. Towards this, authors have

proposed a novel routing protocol. A new field named

as suspicious value is added to the relay nodes routing

table. Then, based on threshold value the malicious

nodes are identified (Su, M. Y. (2011). A trust based

malicious node detection scheme in highly dynamic

networks is presented by defending both the Black-

hole and grey-hole attacks. The trusted nodes ensures

the security in the network (Sargunavathi and Martin

Leo Manickam, 2019).

Authors have proposed an efficient AODV

routing scheme to advance the security in the network

by detecting packet drop ratio at each relay node to

increase the efficacy of the network (Li, J. S., and

Lee, C. T. (2006)).

On the other side, another on demand based

routing protocol was designed to address power

consumption and overhead issues in the network

(Daoud and Rafla, 2019). A new intrusion detection

scheme was developed to interchange data between

the nodes in high mobility networks. Further,

multiclass SVM techniques were included to

minimize the overhead in the network (Arthur, 2018).

On the other side, authors have presented a new

version of AODV to defend various attacks in highly

dynamic ad-hoc network using statistical approach

(Rmayti, Begriche, Khatoun and Gaiti, D 2015).

Similarly, cryptographic based source and destination

nodes detection algorithm is presented to improve the

security in VANETs (Kumar, A., Varadarajan, V.,

Kumar, A., Dadheech, P., Choudhary, S. S., Kumar,

V. A & Veluvolu, K. C., 2021). An efficient

algorithm is presented to defend Black hole attacks in

VANETs based on dynamic threshold value (Malik,

A., Khan, M. Z., Faisal, M., Khan, F., & Seo, J. T,

2022).

Nonetheless, the prevailing on demand intruder

detection approaches designed using one or two

routing the parameters like radio hop count and

direction. To this end, A Secure AODV Routing

Protocol (S-AODV) is proposed. The detained

working of the protocol is exemplified in the next

section.

3 PROPOSED WORK

In this section, secured routing protocol by making

use of traditional AODV to defend both flooding and

black hole attacks in VANETs is explained. In this,

primarily, the cooperative intermediate vehicles

(CRVs) are chosen based on Congestion and Residual

Energy values at each vehicle to avoid unnecessary

transmission (Flooding) of control packets to non-

existent destinations. Thus reduces routing overhead

and routing cost.

Secondly, the optimal secure path to the

destination is identified through trust estimation

among the selected relay vehicles in the network.

Here, the trust value is estimated using distinct

metrics like Hop Count and Network Lifetime. Then,

the vehicles with utmost trust value are preferred to

reduce packet drop rate (Black Hole attack). Thus

improves packet delivery ration and throughput of the

network.

3.1 Selection of CRVs

In the proposed S-AODV, the individual node in the

network hold the 1 hop information of 1-hop

neighbors using HELLO packets, same as traditional

AODV. In addition, in the proposed S-AODV each

node maintain congestion and residual energy

information in the routing table. All the three

parameters like, Hop-count (H), Congestion (C) and

Residual Energy (RE) are obtained using the

following equations given below.

AI4IoT 2023 - First International Conference on Artiﬁcial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry

and Consumer Electronics

448

H = Node Count - 1

(1)

C = αt

(2)

RE = Initial Energy – Final Energy

(3)

From equation (2), the congestion at a vehicle is

obtained by measuring the total number of packets

striking at a rate of ‘α’ over a period t. From equation

(3), residual energy is the energy at a node before and

after simulation. Thus, source vehicle choses the 1

hop neighbouring vehicle with less congestion and

higher residual energy values as relay forwarder.

Therefore, the unnecessary transmission (flooding) of

routing packets can be minimized in the network for

route establishment, in turn reduces flooding attacks.

4 OPTIMAL PATH SELECTION

In the proposed S-AODV, the optimal or finest path

from the several available routes between source to

destination is identified using Trust Value obtained at

each CRV. It is mathematically given by equation (4).

Trust Value = H+NLT

(4)

It is recommended to have less hop-count and

high Network Life Time (NLT), The NLT at each

node is defined as the ratio of Residual Energy at a

node to that of the Energy Consumption Rate at the

same node for any instance of time T. Then, the

obtained Trust Values are stored in the routing tables

of each node in the path selected towards destination.

The routing table is updated on reception of Route

Request packet. The trust value lies in between 0 to 1.

The value 0 shows an untrustworthy vehicle and 1

depicts a trusted forwarder. Therefore, the CRVs with

≤0.5 will be discarded. The latest trust value of a

particular CRV is exchanged with all other vehicles

in the network.

Introduction to the Trust Value:

Importance of Trust estimation

Based on the existing schemes, the vehicles in the

network transmits data to each other without any

centralized administration. Therefore, all the vehicles

behaves as a route and shares network information.

Nonetheless, trusting all the vehicles without

estimating the behaviour may introduce wide variety

of vulnerabilities among fast moving vehicles. Here,

we exemplify the process of selecting suitable route

based on a trust evaluation. In the proposed S-AODV,

the desired route is estimated by incorporating a new

field known as ‘Trust Value’ to S-AODV RREQ

packet. Thus avoids packet transmission through

untrusted vehicles. The S-AODV RREQ packet

consists of the following new fields like:

1. Congestion

2. Residual Energy

3. Initial Energy

4. Final Energy

Algorithm 1: Selection of relay vehicle.

Step1: Initialize the network.

Step2: Procedure-Select the relay vehicle

Step3: Calculate congestion at each vehicle using (2)

Step4: Calculate RE at each vehicle using (3)

Step5: Repeat the step 3 & 4 at each vehicle

Step6: Compare the values of congestion and RE with

threshold values

Step7: Discard the vehicle which violates the

threshold.

Step8: Update the routing table at each vehicle

5 RESULTS AND ANALYSIS

In this section, the proposed S-AODV protocol is

analysed using Network Simulator (NS)-2.34. The

simulation is conducted for 200 vehicles through over

1000m X 1000m area. The range of each node is set to

250m. Table 1 exemplifies the remaining parameters.

Table 1: Network Simulation Parameters.

Parameter

Value

Node densities

0-200

Max Speed

0-100 Kmph

Simulation Time

900sec

MAC Protocol

802.11p

Packet size

512 (bytes)

The performance evaluation of S-AODV over

existing schemes is obtained by considering the

performance metrics like Reliability, Energy Factor,

End to End delay and packet Delivery Ration under

two different scenarios such as, different vehicle

densities and change in vehicle velocities.

Figure 1, shows the variation of reliability over

different vehicle densities in the network for the

proposed S-AODV and existing protocols. The

reliability of S-AODV is superior due to selection of

CRVs through congestion metric and optimal path

selection using trust estimation. Finally, the reliability

in S-AODV is 82%, 83% higher over existing

mechanisms. Figure 2, explains the variation in

energy factor for number of vehicles and is high in

proposed S--AODV over En-AODV and AODV.

An Efﬁcient Secured AODV Routing Protocol to Mitigate Flooding and Black Hole Attack in VANETs for Improved Infotainment Services

449

Figure 1: Variation in reliability for number of vehicles.

Figure 2: Variation in energy factor for number of vehicles.

This is due to selection of CRVs through congestion

metric and optimal path selection using trust

estimation. Therefore, S-AODV has 55%, 56%

higher energy factor over existing schemes.

Figure 3, explains the disparity in delay for

number of vehicles and is less in S-AODV over En-

AODV and AODV. Delay parameter is decreased in

S-AODV by 33% over En-AODV.

Figure 4, explains the enhanced packet delivery

ratio in S-AODV over En-AODV, AODV. This is

due to selection of CRVs through congestion metric

and optimal path selection using trust estimation. It is

further observed that the PDR in S-AODV is

enhanced to 30%, 33% with respect to existing

schemes.

Figure 5, shows the improvement in energy factor

for change in velocity. It can be observed the energy

factor is more in S-AODV over En-AODV and

AODV. This is due to selection of relays through

congestion metric and optimal path selection using

trust estimation.

AI4IoT 2023 - First International Conference on Artiﬁcial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry

and Consumer Electronics

450

Figure 3: Variation in delay for number of vehicles.

Figure 4: Variation in PDR for number of vehicles.

Similarly, figure 6 illustrates that the variation in

reliability for change in velocity and it is observed

enhanced reliability in S-AODV. Further, it is

observed that the reliability in case of S-AODV is

improved to 50% and 53% over existing schemes.

Figure 7 explains the variation in delay metric for

change in vehicle velocity under three protocols.

The delay parameter is less in case of S-AODV.

Similarly, figure 8 shows the variation in PDR for S-

AODV over En-AODV and AODV. Nonetheless, S-

AODV shows enhanced PDR. Overall, the PDR is

enhanced to 11%, 10% in S-AODV over existing

algorithm.

An Efﬁcient Secured AODV Routing Protocol to Mitigate Flooding and Black Hole Attack in VANETs for Improved Infotainment Services

451

Figure 5: Variation in energy factor for change in velocity.

Figure 6: Variation in reliability for change in vehicle velocity.

Figure 7: Variation in delay for change in vehicle velocity.

AI4IoT 2023 - First International Conference on Artiﬁcial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry

and Consumer Electronics

452

Figure 8: Variation in PDR for change in vehicle velocity.

6 CONCLUSIONS

In this work, An Efficient Secure AODV protocol (S-

AODV) is presented to overcome various attacks in

VANET. S-AODV reduces routing overhead and

routing cost through the selection of CRVs. Further,

the optimal secure path to the destination is identified

through trust estimation among the selected relay

vehicles in the network. Here, the trust value is

estimated using distinct metrics like Hop Count and

Network Lifetime. Further, The S-AODV has been

examined using NS-2.34 and results showed that the

S-AODV is superior over existing schemes.

REFERENCES

Shafi, S., & Venkata Ratnam, D. (2022). An Efficient Cross

Layer Design of Stability Based Clustering Scheme

Using Ant Colony Optimization in VANETs. Wireless

Personal Communications, 126(4), 3001-3019.

Singh, R. K., & Nand, P. (2016, April). Literature review of

routing attacks in MANET. In 2016 International

Conference on Computing, Communication and

Automation (ICCCA) (pp. 525-530). IEEE.

Murthy, C. S. R., & Manoj, B. S. (2004). Ad hoc wireless

networks: Architectures and protocols, portable

documents. Pearson education.

AL-Dhief, F. T., Sabri, N., Salim, M. S., Fouad, S., &

Aljunid, S. A. (2018). MANET routing protocols

evaluation: AODV, DSR and DSDV perspective.

In MATEC web of conferences (Vol. 150, p. 06024).

EDP Sciences.

Shafi, S., Mounika, S., & Velliangiri, S. (2023). Machine

Learning and Trust Based AODV Routing Protocol to

Mitigate Flooding and Blackhole Attacks in

MANET. Procedia Computer Science, 218, 2309-

2318.

Su, M. Y. (2011). Prevention of selective black hole attacks

on mobile ad hoc networks through intrusion detection

systems. Computer Communications, 34(1), 107-117.

Sargunavathi, S., & Martin Leo Manickam, J. (2019).

RETRACTED ARTICLE: Enhanced trust based

encroachment discovery system for Mobile Ad-hoc

networks. Cluster Computing, 22 (Suppl 2), 4837-

4847.

Li, J. S., & Lee, C. T. (2006). Improve routing trust with

promiscuous listening routing security algorithm in

mobile ad hoc networks. Computer

communications, 29(8), 1121-1132.

Daoud, L., & Rafla, N. (2019, August). Analysis of black

hole router attack in network-on-chip. In 2019 IEEE

62nd International Midwest Symposium on Circuits

and Systems (MWSCAS) (pp. 69-72). IEEE.

Arthur, M. P. (2018, September). An SVM-based

multiclass IDS for multicast routing attacks in mobile

ad hoc networks. In 2018 International Conference on

Advances in computing, communications and

Informatics (ICACCI) (pp. 363-368).

Rmayti, M., Begriche, Y., Khatoun, R., Khoukhi, L., &

Gaiti, D. (2015, August). Flooding attacks detection in

MANETs. In 2015 International Conference on Cyber

Security of Smart Cities, Industrial Control System and

Communications (SSIC) (pp. 1-6). IEEE.

Kumar, A., Varadarajan, V., Kumar, A., Dadheech, P.,

Choudhary, S. S., Kumar, V. A. & Veluvolu, K. C.

(2021). Black hole attack detection in vehicular ad-hoc

network using secure AODV routing

algorithm. Microprocessors and Microsystems, 80,

103352.

Malik, A., Khan, M. Z., Faisal, M., Khan, F., & Seo, J. T.

(2022). An efficient dynamic solution for the detection

and prevention of black hole attack in

vanets. Sensors, 22(5), 1897.

An Efﬁcient Secured AODV Routing Protocol to Mitigate Flooding and Black Hole Attack in VANETs for Improved Infotainment Services

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